Keywords:
Abstract: The rapid evolution of industrial automation and smart manufacturing has created an urgent need for standardized education in automation engineering. The International Electrotechnical Commission (IEC) standards play a critical role in ensuring interoperability, safety, and efficiency in industrial systems. However, integrating IEC standards into automation engineering curricula presents significant challenges, particularly in adapting to emerging technologies and meeting industry demands. This keynote will talk the challenges faced in driving IEC standardization and integrating new technologies into the Automation Engineering program at King Mongkut’s Institute of Technology Ladkrabang (KMITL), Thailand.

Keywords: Nonlinear Control, Mechanical Systems Control, Guidance and Flight Control
Abstract: In this paper, we propose a route guidance method that enhances safety by combining route guidance based on Control Lyapunov Functions (CLFs) and Zeroing Control Barrier Functions (ZCBFs). This approach achieves obstacle avoidance by accounting for the geometry of both the vehicle and the obstacles. In conventional CBF-based collision avoidance control, solving a quadratic programming problem to calculate the distance between the vehicle and obstacles is computationally demanding for real-time applications. Furthermore, the control system for the four-wheeled vehicle in this study includes non-convex input constraints due to steering angle limitations. To address these challenges, the proposed method approximates the quadratic programming problem by using linear programming based on a 1-norm to calculate the distance between the vehicle and obstacles. Additionally, the input space is divided into two regions, with computations performed separately for each, in order to handle the non-convex input constraints. The overriding value calculation in CBF is also conducted using 1-norm optimization, ensuring the system's safety. The effectiveness of our proposed method is confirmed through computer simulations.

Keywords: Robotic and Automation Systems, Guidance and Flight Control, Mechanical Systems Control
Abstract: This paper proposes an obstacle avoidance control method based on Monte Carlo Model Predictive Control (MCMPC), using samples generated by the C/GMRES method. MCMPC, a sample-based approach, is less prone to local minima, making it suitable for non-convex problems like mobile robot navigation. However, MCMPC tends to produce oscillatory control inputs that can burden actuators. To mitigate this, the paper generates samples based on smooth input trajectories from the C/GMRES method, reducing oscillations while exploring global solutions. Even when the solution converges to a local optimum, the method can find alternative global solutions, avoiding deadlock. This paper validates the proposed method through comparisons with conventional approaches.

Keywords: Robotic and Automation Systems, Mechatronics Systems, Human Interfaces
Abstract: In crowded urban environments, service robots need to guide pedestrians safely while avoiding them. Traditional approaches such as artificial potential field methods enable real-time path planning at low computational cost, but they assume full visibility of pedestrians using on-board sensors such as LiDAR and cameras. In practice, these sensors suffer from occlusion and limited field of view, reducing safety and reliability. To overcome this limitation, we propose a novel pedestrian-aware navigation system that combines a drone-based bird's-eye view with YOLOv8 object detection and artificial potential method. The drone captures overhead images of the environment, from which YOLOv8 detects the positions of pedestrians and mobile robots in real time. The geometric relationship between the drone and the ground is modeled using a pinhole camera model to accurately calculate the coordinates of the pedestrian. These coordinates are transmitted to the robot via ROS, enabling global situational awareness and safe path planning even when on-board sensors fail due to occlusion. Experimental validation demonstrates that the proposed system accurately detects all pedestrians in the vicinity and enables early and effective collision avoidance. The robot successfully navigates complex scenarios in which pedestrians suddenly appear or do not have direct line-of-sight contact, validating the effectiveness of the proposed drone-assisted navigation system.

Keywords: Robotic and Automation Systems, Mechatronics Systems, Human Interfaces
Abstract: This paper proposes a recognition method for road surface guidance signs that provide pedestrians with easy-to-understand directions and, based on the recognition results, a method for autonomous mobile robots to predict pedestrian movements and generate routes safely and efficiently. The robot recognizes road surface guidance signs using image processing. The direction indicated by a road surface guidance sign is detected by line detection and arrow detection. The direction of the sign and the direction of pedestrian movement recognized by 2DLiDAR are used to calculate the degree of match that indicates the degree to which pedestrians are currently following the sign. The potential method is used for path planning. By extending the repulsive force in the direction indicated by the sign and using the calculated agreement as a weight, the robot can safely avoid pedestrians who are likely to follow the sign.

Keywords: Robotic and Automation Systems, Autonomous Decentralized Systems
Abstract: This paper proposes an occlusion-aware pursuit control scheme in 2D environments, where obstacles are represented as point cloud data. To enable occlusion avoidance, obstacles are approximated by ellipses fitted via kernel-based support vector machines and adaptively updated based on the robot and target configuration. The resulting control law is formulated using control barrier functions, ensuring occlusion avoidance without prior knowledge of obstacle geometry.

Keywords: Robotic and Automation Systems
Abstract: For autonomous navigation, mobile robots must avoid collisions with dynamic obstacles such as pedestrians. In previous work, we proposed a motion planner based on a CNN with RGB and depth image inputs. To enable the robot to plan avoidance motions while considering the moving direction of an obstacle, in this paper, we propose a motion planner that utilizes optical flow images as inputs. Through autonomous navigation experiments, we demonstrate that the proposed motion planner enables the robot to successfully avoid both dynamic and static obstacles.

Keywords: Human-Machine Systems, Robotic and Automation Systems, Medical and Welfare Systems
Abstract: Safely manipulating human-worn clothing is a key challenge in robotic dressing assistance for aging populations. This paper presents a novel compliant gripper concept specifically for trousers, targeting key issues in physical human-robot interaction (pHRI) safety, adaptability to varied fabrics and body shapes, and reliable operation in confined spaces. Our proposed hybrid gripper integrates a roller gripper module, with a compliant anthropomorphic linkage finger. This synergistic design ensures secure trouser grasping while finger mechanical compliance and a roller module suspension system provide inherent safety. A 3D-printed prototype was produced to validate the concept using bio-compatible materials. Overall, the proposed gripper contributes to bridging critical gaps in assistive dressing technology, paving the way for safer and more effective robotic dressing support.

Keywords: Biological and Physiological Engineering, Human-Machine Systems, Medical and Welfare Systems
Abstract: Mechanical vibration stimulation applied to human muscles induces kinesthetic illusions (KI) and tonic vibration reflexes (TVR) which affect human motor behavior. These effects have attracted increasing attention in the field of rehabilitation, particularly for individuals with impaired motor functions. Lateral trunk bending, a crucial component of postural control, is involved in many daily activities. However, the effects of vibration stimulation on trunk muscles involved in lateral bending have not studied sufficiently. This research explores the potential of mechanical vibration stimulation as a rehabilitation approach by targeting trunk muscles associated with lateral bending. Induced KI and TVR responses are experimentally evaluated, and the effects of varying vibration frequencies are compared to assess differences in motor responses.

Keywords: Virtual Reality Systems
Abstract: Dynamic difficulty adjustment (DDA) systems provide users with an optimal level of challenge. Previously, some studies developed a DDA system that simply adjusts the difficulty up or down according to the number of successes, only balancing the difficulty to a success rate of 50%. However, setting the difficulty only to a moderate level of 50% is insufficient since the appropriate difficulty varies depending on the individual and the situation. It is preferable to set stepwise difficulty levels according to the user's skill to adjust the difficulty between them and to evaluate the difficulty setting while considering the psychological aspects of the user. For this purpose, we propose an adaptive difficulty setting that consists of stepwise difficulty levels (e.g., hard, normal, and easy) adaptive for each user and evaluate it using self-efficacy with successful experiences. In the experiment, we employ a Kendama game in a VR space where difficulty can be easily adjusted to objectively and subjectively evaluate the adaptive difficulty setting with one in which difficulty levels are uniformly fixed for all users. We demonstrate that the adaptive difficulty setting is stepwise difficulty levels that reflect each user's skill and that they may be consistent with subjective difficulty levels. It is also possible to create a feedback system that makes subjective modifications to the stepwise difficulty levels even if they do not match subjective difficulty levels for future work.

Keywords: Virtual Reality Systems, Electrophysiology and Kinesiology, Medical and Welfare Systems
Abstract: In recent years, virtual reality (VR) has garnered attention in the fields of sports and rehabilitation, with reports highlighting its effectiveness not only on physical aspects but also on mental and cognitive functions. While the use of VR’s immersive qualities and its potential to reduce the mental burden of exercise is expanding, its effectiveness often relies heavily on the skills of its developers. A systematic understanding of the utility of VR experiences is therefore necessary. This study investigated whether the widely reported phenomenon of underestimation in VR extends to perceived time and perceived task counts. Experimental results showed that in VR environments, participants underestimated the number of play counts to 0.53 times the actual number (p < 0.01), while perceived time was approximately consistent with actual time at 1.19 times. In real-world environments, perceived counts were 0.91 times the actual number, and perceived time was significantly overestimated to 1.56 times the actual time. Additionally, some participants highly rated their sense of body ownership even under the same environmental conditions, and these individuals tended to perceive time and task counts more accurately. This suggests that enhancing the sense of body ownership could potentially provide a means to control such underestimation phenomena. These findings suggest that VR has the potential to contribute to reducing cognitive burdens associated with exercise experiences.

Keywords: Adaptive and Optimal Control, Mechanical Systems Control, Nonlinear Control
Abstract: Sampling-based stochastic model predictive control (SMPC) is one of the promising methods for nonlinear systems because it is gradient-free, and the current parallel computation performance is much higher. The model predictive path integral (MPPI) is an SMPC that shows the effectiveness in the field of quadrupedal control, but not six-legged. This paper proposed MPPI-based body pose control for six-legged locomotion based on Follow-the-Contact-Point (FCP) gait control which is a quasi-static gait control. Especially, an induction cost is introduced to induce the FCP gait control mode switching. As a result, the MPPI-based body pose control handles the inconsistency between the reference trajectory and the next foothold. Our six-legged robot showed adaptability to rough terrain, but its limitation was also shown.

Keywords: Robotic and Automation Systems
Abstract: Utilizing structural joint stops to improve dynamic performance of a robot while in a compact and light structure has been proposed by authors. In this paper, first the control of landing motion for a one-legged robot consisting of a hip joint and a knee joint with a joint stop mechanism at each joint is described. The motion is generated by the optimization calculation to realize a soft landing. The joint stops’ effectiveness by using the reaction torque generated by the mechanism are confirmed by both simulation and experimental results. And hopping motion as well as start motion for a biped robot consisting of two hip joints and two knee joints with a joint stop mechanism at each joint are generated to realize far and high hopping motions. The effectiveness of joint stops in hopping motions is also confirmed by simulation results.

Keywords: Robotic and Automation Systems
Abstract: Prolonged periods of immobility can lead to the formation of blood clots, a condition known as economy class syndrome or deep vein thrombosis (DVT). There is a high risk of DVT in confined spaces, including long flights, buses and evacuation centers. Prior studies have introduced electrostimulation devices that are clinically proven to promote blood flow. However, these devices cannot be reused and must be attached to the appropriate position by the user. Other types of massage devices, including stationary and wearable massagers, have been developed; however, they require installation space, wearing motion, and soundproofing measures in a small space, and have portability issues. To address these challenges, this study presents a small, snake-shaped robot that performs a compression motion by coiling itself around a target through shape control. In this paper, we propose a modeling method for controlling the robot’s shape to enable coiling around the target and a method for compression motion control aiming at a target pressure of 30-60mmHg—similar to the intermittent pneumatic compression (IPC) device used in hospitals. Through the experiment, we confirmed that the results for the coiling motion revealed that the robot coiled around the target by curling its body with increasing tension. As for the compression motion, the compression also increased with increasing tension, eventually reaching an average of 30mmHg.

Keywords: Robotic and Automation Systems
Abstract: Bridges often feature beams, which make it challenging for conventional bridge inspection mobile robots with wide wheel spacing to traverse them. In this study, we developed a flexible wheel diameter mechanism for such robots in which the wheels expand upon encountering uneven surfaces and contract during wall climbing to improve energy efficiency and stability. After determining the required wheel diameter for effectively traversing beams, we designed a wheel mechanism that incorporates a radially extendable pantograph mechanism and a Hoberman mechanism. The proposed mechanism allows for expansion under propeller thrust. To evaluate its performance, we conducted experiments on wheel expansion under expected propeller thrust conditions and measured power consumption differences based on variations in wheel diameter. The results confirmed that wheel expansion facilitated the traversal of uneven surfaces for the robots, while wheel contraction improved energy efficiency during movement.

Keywords: Manufacturing Systems, Process Automation, Factory Automation
Abstract: Many international standards related to energy management have been published by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC). These standards were developed separately by different technical committees within ISO and IEC, but organizations refer to them in combination. This paper introduces these international standards and presents application cases from some organizations. Recently, in addition to energy management, organizations have been required to address carbon management as actions against global warming. In response to this situation, some international standards related energy management and efficiency are being mentioning carbon management. This paper describes considerations for future international standards.

Keywords: Manufacturing Systems, Factory Automation, Innovative Systems Approach for Realizing Smarter World
Abstract: 　This research aims to realize a decarbonized society by utilizing the standardization of industrial facility energy management systems (FEMS) and applying them to our own factory (Omron AYABE Factory). To improve energy productivity (EP100), we have developed and implemented a model for visualizing the energy flow of the entire factory, introducing renewable energy, and unified monitoring and forecasting of both the energy supply and demand. As a result, the prediction accuracy was confirmed to be within 5%, and by applying the optimized supply and demand plan, it was estimated that the factory's energy costs could be reduced by 24.3% over 20 years, along with a 53.6% reduction in CO2 emissions. Going forward, we aim to create KPIs that lead to factory management incentives to accelerate the evolution of energy demand forecasting models and the social implementation of FEMS. We believe that this initiative will contribute to the demonstration and advancement of energy management systems based on the international standard IEC 63376: 2023.

Keywords: Adaptive and Optimal Control, Manufacturing Systems, Factory Automation
Abstract: This paper presents a common information model (CIM) for the energy management of industrial facilities. In an effort to promote the adoption of renewable energy and achieve carbon neutrality, there is an increasing need to manage carbon footprints of products (CFP) in the manufacturing industry. Industrial facility energy management systems (FMES) are being introduced as an international standard by the International Electrotechnical Commission (IEC) as IEC 63376:2023. It is essential to define and standardize the information model for manufacturing facilities alongside the CIM format, in order to accurately reflect the current energy performance and efficiency of energy supply, storage and energy conversion equipment. Each piece of equipment is represented in the CIM format and applied to optimize a daily operational energy management planning.

Keywords: Manufacturing Systems, Factory Automation, Information Management Systems
Abstract: We have been conducting a study of realizing carbon neutral society by smart industrial energy management systems. The paper reports recent status of initial design of the smart industrial energy management system testbed including the testbed architecture, the scenario of the energy management data coordination between factory and energy management center emulated on the testbed. Latest unit experiment result is also reported.

Keywords: Process Automation, Factory Automation, Information Management Systems
Abstract: Against global warming, factories are required to increase their use of renewable energy and to improve their energy efficiency. For the former requirements, they introduce renewable energy power generation facilities and purchase the power generated by them. As one of the measures of the latter requirement, to revise facility operation is focused. Japan Electronics and Information Technology Industrial Association (JEITA) Control and Energy Management Committee defines the efficient operation of energy supply and demand facilities as “RENKEI control” and has been promote over 10 years. In addition, JEITA is promoting “RENKEI control” in the Association of Southeast Asian Nations (ASEAN) countries under the Cleaner Energy Future Initiative for ASEAN (CEFIA), which is supported by the Ministry of Economy, Trade, and Industry (METI) in Japan. These activities include potential survey of effects of the RENKEI control, education for students and energy managers in the countries and development of assessment tools. This paper introduces the outlines and effects of JEITA’s the RENKEI control promotion activities through CEFIA.

Keywords: Mechanical Measurement
Abstract: Compressed molded products (tablets) made by compressing powder are used in a variety of fields, chemical, food and pharmaceutical. The tablet hardness is usually evaluated by the force (crushing force) at which the tablet is pressed with the pestle and crushed. We are now conducting research into the evaluation of tablet hardness, when the tablet size is changed, there is a significant change in the force required for tablet compression and the crushing force. In this paper, we will explore methods suitable for comparing the hardness when changing the height and diameter of tablets, additionally, the compression force during tableting with various shapes is also examined.

Keywords: Mechanical Measurement
Abstract: Tablets made by compressing powder are used in a variety of fields, and we are now conducting research into the evaluation of tablet hardness. The tablet hardness is usually evaluated by force that the tablet broke when the indenter is pressed in, however, the performance for impact forces is also important from the perspective of transportation. In this research, we manufactured an impact testing device, collided a steel ball with a tablet, and measured the impact force acting on the tablet. These are compared with the crushing force measured using the hardness tester, and the tablets hardness examine from both the performance for indentation and impact.

Keywords: Mechanical Measurement, Sensors and Transducers
Abstract: To evaluate the mechanical properties of various materials, advanced force standards that ensure traceability and international equivalence are essential. While most standards focus on static forces, there is an increasing need to measure dynamic forces in industries such as automotive and aeronautics. We are developing a device that generates and measures sinusoidal waves to validate the principles of dynamic force generation and measurement. Our goal is to measure forces ranging from 100 N to 1 kN at frequencies from 20 Hz to 2.5 kHz, with a target uncertainty of a few percent.

Keywords: Virtual Reality Systems, Identification and Estimation, Modeling, System Identification and Estimation
Abstract: This paper contributes to develop the surgical training simulator with force feedback function for chiseling a hard tissue using a bone chisel and a mallet. In most of previous studies on surgical training simulators have focused on soft tissues. However, in orthopedic surgery, surgeons manipulate hard tissues such as bones and teeth. Therefore, the development of the surgical training simulator for chiseling has been promoted. In order to enhance realistic sensation inthe simulator, it is required that the chiseling process which involves elastic-plastic deformation is simulated accurately by the dynamical model of the chiseling motion. As such a model, the previous study proposed the spring-mass-damper model with a varying equilibrium point driven by the chiseling force. In this study, we conduct the comparative evaluation between that previously proposed model and the Maxwell model, which can represent viscoelastic deformation. In addition, the two chiseling models are compared with the experimental results of actual chiseling motion obtained using the measurement system.

Keywords: Modeling, System Identification and Estimation, Process Automation, Robotic and Automation Systems
Abstract: This study contributes to an advanced control system for a high-accuracy automatic pouring machine used in the casting industry. The automatic pouring machines have been introduced recently to improve the working environment of the pouring process. The previous studies have shown that improving the accuracy of the pouring flow rate enables precise control of key process variables such as the outflow liquid's falling position from the ladle, the liquid's weight filled in the mold, and the sprue cup's liquid level. In recent years, the advanced feedforward type pouring control with online parameter identification has also been proposed．　However, it is difficult to implement this approach in practical use because it takes a time over the required tact time. Therefore, we propose the fast and accurate model parameters identification for the feedforward type pouring control. In this approach, the model parameters can be identified accurately in a short time by combining hierarchically a Newton-Raphson method and a Gauss-Newton method. The efficacy of the proposed approach is verified by the simulations using the mathematical model of the automatic pouring machine.

Keywords: Identification and Estimation, Nonlinear Control, System Engineering
Abstract: Floating Offshore Wind Turbines (FOWTs) are vital for deep-sea renewable energy. Unlike onshore turbines, they face complex wind, wave, and platform interactions, generating periodic noise and makes it difficult to control. Current blade pitch control mainly uses rotor speed, however, introducing nonlinear optimal control offers potential improvements by more effectively handling these complexities. While some research explores alternative control and state estimation for FOWTs, accurate real-time dynamic state assessment remains challenging due to system nonlinearities and environmental uncertainties. By descriving FOWTs with nonlinear state equations, we can introduce an FFT-enhanced nonlinear Kalman filter (we explored three versions), and nonlinear optimal control inputs derived using the Hamilton-Jacobi equation and the stable manifold method, developing a model geared towards robust control. Three types of nonlinear Kalman filters are the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Cubature Kalman Filter (CKF).

Keywords: Identification and Estimation, Intelligent Systems, Modeling, System Identification and Estimation
Abstract: This paper proposes a model based on supervised learning for fault detection and diagnosis of a SISO magnetic levitator (MagLev) system. The approach considers multiple combinations of feed forward neural networks that include multilayer neural networks, signal filters, and autoregressive neural network with exogenous input (NARX) as a previous input for the network. Faults such as bias, noise, and equilibrium change located in the sensor, actuator, and plant are combined as a benchmark to be detected in real-time to evaluate this proposal. Therefore, the whole system considers one input signal, one transfer function, two types of filters, one NARX neural network, and six feedforward neural networks, where each output is associated with a specific type of fault. The proposed model manages to estimate the faults presented with an accuracy higher than 95%.

Keywords: Guidance and Flight Control, Adaptive and Optimal Control, Multivariable Control
Abstract: Reproducing human-like maneuvering behavior is desirable for autonomous ships to ensure safe and cooperative operation. This paper proposes a control method for ship approaching maneuvers that reproduces human-like maneuvering characteristics. The maneuver was divided into speed control and turning phases, and the actuator operations in each phase were optimized based on a nonlinear ship motion model. A model-based control system was developed by combining these optimized commands with state feedback control. Simulation under a realistic berthing scenario confirmed accurate control of the approach maneuver.

Keywords: Modeling, System Identification and Estimation, Nonlinear Control, Power Systems Control
Abstract: This scholarly investigation delineates a comparative simulation assessment of the Power Balance Cascade Control Technique (PBCCT) compare with traditional Proportional-Integral (PI) Cascade Control methodologies in the regulation of a Three-Level Buck Converter (TLBC) under the conditions of Constant Resistance Load (CRL), Constant Current Load (CCL), and Constant Power Load (CPL). PBCCT, grounded in the principle of energy conservation, utilizes a cascade control structure with an outer voltage loop and an inner current loop to achieve enhanced power regulation. The simulation results reveal that PBCCT delivers faster dynamic response, reduced voltage ripple, and improved system stability across all load scenarios. Unlike the PI Cascade Control, which exhibits oscillations and slower settling behavior under varying loads, PBCCT effectively maintains robust performance, making it a compelling solution for high-efficiency DC-DC power conversion in systems subjected to dynamic load profiles.

Keywords: Modeling, System Identification and Estimation, Adaptive and Optimal Control
Abstract: The research proposed an innovative method to enhance the efficiency of inductive wireless power transfer (IPT) systems with a power rating of 1.5 kW operating at a frequency of 20 kHz. This method utilizes Design of Experiments (DOE), Central Composite Design (CCD), and Finite Element Method (FEM) to optimize coil design parameters. The results indicate that the ideal IPT efficiency achieved is 93.45%, and the system efficiency of the IPT is 82.71%. These efficiencies were maintained with an offset distance of ±40 mm in the left-right direction (x), the up-down direction (y), and 120 mm in the distance between the transmitter (Tx) and receiver (Rx) (z). The CCD technique effectively reduces the experimentation time while still providing comprehensive and optimal solutions. Additionally, FEM aids in the development of the most efficient IPT system before creating a real-world model.

Keywords: Adaptive and Optimal Control, Transportation Systems, Intelligent Control
Abstract: This paper presents a model predictive control (MPC) design approach for the thermal control of cylindrical Li-ion battery packs. The realistic time-varying nature of Li-ion battery cell's parameters is taken into account with the core-surface thermal model of cylindrical cells. In this model, the battery charge-discharge current is regarded as a time-varying thermal input disturbance. As a result, a quadratic programming (QP) problem is formulated to find the optimal profile of the cooling-heating flow in the whole battery operation period. MPC is then used for this QP problem to account for the problem trackability caused by its high dimension. Simulations with a realistic driving pattern then clearly shows the effect of the time-varying battery parameters to the obtained cooling-heating performances.

Keywords: System Engineering
Abstract: Electric vehicle (EV) technology has become a transformative force in the automotive industry, driving the demand for efficient and compact power conversion systems. Among the various topologies, the Quadratic Boost Converter (QBC) is widely adopted due to its high voltage gain and relatively simple structure. This paper presents a study on power loss estimation in a QBC circuit, focusing on both switching and conduction losses. Analytical equations were used to estimate losses, while experimental validation was conducted using a prototype converter operating at 12 V input and 48 V output. The measurement results indicate that switching losses, which include both conduction and dynamic switching losses in the MOSFET, increase significantly with load current. In contrast, losses in other passive components—such as inductors and diodes—remain relatively constant. The findings provide insight into dominant loss contributors under varying load conditions, supporting further optimization of QBC designs for EV and other high step-up applications.

Keywords:
Abstract: Humans process a vast amount of information from the inside and outside of their bodies and take actions according to the situation. Even in everyday actions such as walking, a large amount of information from the senses of sight, hearing, touch (from the soles of the feet), and muscle sensation is unconsciously integrated to produce smooth coordinated movement of the arms and limbs. On the other hand, when people take up a new sport or rehabilitation, they need to be aware of how to move their bodies in a way they have never done before and consciously change their behavior. In addition, when they are unable to perform actions that they were previously able to do unconsciously due to illness or aging, or when their bodies do not move as they expect, a sense of discomfort becomes apparent. To realize robot-mediated human support, it is necessary to understand the characteristics of human biological signals and to quantitatively capture “awareness” given to humans via robots and “discomfort” that should be supported by robots. In this presentation, I would like to discuss research strategies to quantitatively capture the state of humans based on measurement of biological signals.

Keywords: Multivariable Control, Networked Sensor System, Modeling, System Identification and Estimation
Abstract: Two switching observer (SO) schemes have been proposed recently in order to address unknown disturbance and measurement dropout. Each of them has its merit and drawback, depending on conditions of the signal loss pattern, disturbance dynamics, and system matrices. This paper discusses these drawbacks and whether or not they can be remedied by a hybrid approach taking advantage of each.

Keywords: Intelligent Control
Abstract: In this position paper, we introduce an event-triggered Adam-based method for multi-agent systems, where agents collaboratively minimize the sum of their local nonconvex objective functions. Each agent has the estimates of the locally optimal solution as well as the first and second moments of the global gradient. These estimates are shared with neighboring agents and updated using a distributed Adam algorithm. Instead of communicating their estimates at every iteration, agents exchange information with their neighbors only sporadically based on an event-triggered communication scheme. Through numerical experiments, we show that agents' estimates converge to a neighborhood of a locally optimal solution.

Keywords: Autonomous Decentralized Systems
Abstract: Locust plagues have posed a severe threat to agriculture since ancient times, with no fundamental solution yet discovered. While insecticides are currently the primary control method, effective intervention is limited to the nymph stage, and concerns remain regarding their impact on ecosystems and human health. This necessitates the development of alternative strategies for locust swarm management. Recent research identified 4-vinylanisole (4VA), a volatile organic compound produced by the migratory locust (Locusta migratoria), as an aggregation pheromone. It also discovered OR35, an olfactory receptor specifically responsive to 4VA. Focusing on this aggregation pheromone and OR35, this study aims to analyze the impact of OR35-deficient individuals on swarm dynamics through simulation. To achieve this goal, we developed a mathematical model incorporating the effects of OR35-deficient individuals on swarm dynamics, building upon established self-propelled particle models and empirical findings on locust movement patterns. Our simulation results revealed trends in the proportion of OR35-deficient individuals required to cause swarm collapse, providing insights into non-insecticidal methods for controlling locust swarms.

Keywords: Autonomous Decentralized Systems
Abstract: Set-net fishing often captures multiple fish species simultaneously, necessitating labor-intensive sorting and leading to lower survival rates for released fish. To mitigate these issues, this study examines how differences in swimming speed affect the behavior of heterogeneous fish schools in a rectangular set-net environment. By extending a schooling model to incorporate species-specific parameters, we conducted simulation experiments with two fish species. Our results reveal that differences in swimming speed can lead to spontaneous spatial separation of the schools. These findings shed light on the critical parameters underlying mixed-species dynamics and offer guidance for designing set-net structures and operational strategies aimed at selective capture, with the potential to contribute to the efficient and sustainable use of fisheries resources.

Keywords: Discrete Event Systems
Abstract: This paper considers a multi-robot system where a task described by a syntactically co-safe liner temporal logic formula is given for each robot. We propose an arbitration mechanism to complete all tasks of all robots without collisions between robots. A local controller of each robot determines a preference list of events to complete its task and the arbitration mechanism selects an event for each robot to avoid the collisions. The mechanism consists of a supervisor and an arbitrator. The supervisor computes a set, called a control pattern, of event profiles that describe a permissive event for each robot to achieve the collision avoidance. The arbitration determines an optimal event profile in the control pattern based on the preference lists of all robots.

Keywords: Robotic and Automation Systems, Factory Automation
Abstract: This paper presents an underactuated robotic hand designed for handling and manipulating garments or fabric articles across a variety of scenarios. The proposed device features a single articulating finger and a rotatable suction mechanism, which enables the flexible extraction of individual fabric layers from a stack or pile. The robotic finger is capable of grasping fabric at arbitrary points through double-sided contact, lifting fabric laid flat on a surface, and separating a single fabric layer from surrounding layers. This design enables automated garment-handling systems to perform multiple post-processing tasks—such as screen printing, ironing, and packaging—using a single end-effector. A prototype was fabricated through metal machining and mounted on a robotic arm. Experimental results confirm the effectiveness of the proposed approach.

Keywords: Mechatronics Systems, Robotic and Automation Systems
Abstract: This paper proposes a novel cooperative drone-based system for the maintenance and inspection of offshore wind turbines. Current inspection techniques face limitations such as high costs, safety risks, and operational instability, particularly due to wind disturbances. The proposed system integrates multiple interconnected drones to enhance vertical mobility and stability, enabling both contact and non-contact inspection methods. The design is validated through a scaled mockup of a wind turbine, with preliminary experiments demonstrating the feasibility of the control methods. Results suggest that the proposed system offers a promising solution for efficient, safe, and scalable offshore wind turbine inspections.

Keywords: Robotic and Automation Systems
Abstract: This paper proposes a shared control support system for electric wheelchairs, aiming to enhance the control experience for individuals with mobility impairments. Traditional joystick control requires dexterous hand movements and high cognitive ability, making it difficult for elderly users with motor or cognitive impairments. Many existing shared control systems focus on obstacle avoidance, but these approaches perform poorly in environments with limited clearance, such as door gaps. To address this, we introduce a system that predicts the user’s intention based on past wheelchair trajectories, without the need for prior environmental information. The system employs a gap-based local path planner that detects immediate surrounding gaps using a 2D LiDAR sensor, predicting the intended gap of the user and guiding the wheelchair through it. To prevent false positives, a ”No Gap” hypothesis is used when the user do not intend to go through any of the found gaps. This approach ensures the system provides support that aligns with the user’s intention, improving control in tight or clustered environments.

Keywords: Medical and Welfare Systems, Robotic and Automation Systems
Abstract: With the growing shortage of caregivers, the demand for independent support technologies is increasing. Yorisoi Robot, a rail-guided assistive device, helps individuals with walking disabilities lead independent lives by mitigating fall impacts without restricting body movement. However, it requires manual transportation by caregivers before use, which may prevent users from accessing Yorisoi Robot independently. In this study, we propose an autonomous transport robot that delivers Yorisoi Robot to users, enabling access without the need for caregiver manual transportation. Experimental results demonstrated that the system successfully delivered Yorisoi Robot under various conditions.

Keywords: Signal and/or Image Processing, Identification and Estimation, Electrophysiology and Kinesiology
Abstract: As amateur golf grows, professional coaching remains costly and inaccessible for many. The system analyzes face-on video input, segments swings into eight phases with SwingNet’s convolutional-recurrent model, and evaluates 33 pose keypoints via Mediapipe Pose. Expert-informed biomechanical rules assess metrics like spine angle, stance width, arm alignment, and head movement. Instant feedback—visual metrics, looped replay, and motivational audio via GPT-4o-mini-tts—is generated through GPT-4.1 summaries, offering specific tips (e.g., “keep your spine angle flatter at the top,” “widen your stance for stability”). By automating high-quality instruction, this scalable system makes personalized golf coaching affordable and widely available.

Keywords: Robotic and Automation Systems, Human-Machine Systems, Signal and/or Image Processing
Abstract: This paper presents a hand gesture-controlled robotic system for laparoscope manipulation during minimally invasive surgery (MIS). Using MediaPipe-based gesture recognition, neural network gesture classification model, and a UR3e robotic arm, surgeons can adjust laparoscope orientation intuitively—tilting and rotating without physical contact. The system includes a hand gesture interface that allows users to adjust command magnitude, directly affecting the robot’s speed. Additionally, the UR3e arm uses the Tool Center Point (TCP) function to prevent collisions at the entry point. The system also leveraging YOLOv11 and Endo-Depth model for real-time depth estimation to prevent tissue collisions. Performance was evaluated through task completion accuracy, system delay, and gesture efficiency.

Keywords: Innovative Systems Approach for Realizing Smarter World, Human Interfaces, Human-Machine Systems
Abstract: Shopping plays a significant role in shaping consumer identity and social integration. However, for individuals with visual impairments, navigating in supermarkets and identifying products can be an overwhelming and challenging experience. This paper presents an AI-based shopping assistant prototype designed to enhance the autonomy and inclusivity of visually impaired individuals in supermarket environments. The system integrates multiple technologies, including computer vision, speech recognition, text-to-speech synthesis, and indoor navigation, into a single, user-friendly platform. Using cameras for ArUco marker detection and real-time environmental scanning, the system helps users navigate the store, identify product locations, provide real-time auditory guidance, and gain context about their surroundings. The assistant interacts with the user through voice commands and multimodal feedback, promoting a more dynamic and engaging shopping experience. The system was evaluated through experiments, which demonstrated its ability to guide users effectively and improve their shopping experience. This paper contributes to the development of inclusive AI-driven assistive technologies aimed at enhancing accessibility and user independence for the shopping experience.

Keywords: Human-Machine Systems, Robotic and Automation Systems, Human Interfaces
Abstract: Robotic systems in unstructured environments, such as homes or logistics hubs, face challenges in intuitive human-robot collaboration, contextual comprehension, and versatile object manipulation. We present a novel solution integrating a Universal Robots (UR) arm with a vacuum gripper, guided by a sophisticated vision-language-action model. Our unique multi-agent AI framework, comprising planner, worker, and supervisor agents coordinated through a shared state, enables complex task decomposition, real-time object tracking, and adaptive execution, requiring minimal hardware and pre-training compared to traditional reinforcement learning agent or mechanically limited systems. The vacuum-based end-effector ensures material-agnostic manipulation, enhancing versatility. Real-world testing demonstrates robust performance, achieving 90.91% planning and execution accuracy across diverse tasks and 63.64% error resilience, while the cognitive layer was evaluated using 100 test cases with text instructions for language model assessment. This work advances natural language-controlled robotics, offering a scalable, intuitive foundation for adaptable robotic assistants in practical settings.

Keywords: Nonlinear Control
Abstract: In April 2020, the revised Road Traffic Law and the revised Road Trucking Vehicle Law went into effect in Japan, allowing automated driving systems to replace all driving under certain conditions. This has encouraged the development of automated driving systems in Japan. Research on automated driving is being conducted for various situations such as driving, parking, detouring at intersections, etc. In this research, we will study the ”emergency avoidance” among those situations. The goal is to develop an algorithm that can be applied to real vehicles. A simulator is used to evaluate and improve the algorithm. Currently, two approaches can be taken: control approach or machine learning approach. The former is pursued here. The situations assumed at this stage are avoidance of collision with other vehicles coming out from an intersecting road between buildings, oncoming vehicles in which the driver may fall asleep at the wheel, and pedestrians. Currently, when other vehicles are detected, the time required until they collide with each other is calculated and compared for each sample, the avoidance point having the largest margin is searched, and the steering angle is determined to follow the trajectory of the collision avoidance. In real-world systems, however, the input is torque, not steering angle. Due to this difference, treating the steering angle as the input may idealize the system response and overestimate its performance. Therefore, in this study,

Keywords: Adaptive and Optimal Control, Nonlinear Control, Identification and Estimation
Abstract: This paper proposes an adaptive LMS framework integrated with phase compensation to address multi-source disturbances in rotational systems dominated by LuGre friction dynamics. The LuGre model’s nonlinear characteristics—including presliding hysteresis, Stribeck effects, and stick-slip transitions—induce velocity-dependent disturbances that interact with external periodic disturbances. A adaptive LMS architecture is developed to simultaneously estimate friction-induced oscillations and external harmonic disturbances through parallel adaptive filters. Key innovations include a model-free phase lead compensation mechanism counteracting the combined phase lag from LuGre friction and system dynamics, and a multi-frequency suppression structure resolving disturbance harmonics generated during low-velocity crawling. Simulation tests demonstrate significant reduction in tracking error compared to conventional PID control, with spectral analysis showing effective attenuation of both external disturbances and friction-induced vibrations.

Keywords: Nonlinear Control, Robust Control, Safety, Environment and Eco-Systems
Abstract: This paper introduces a novel arc sine-based Barrier Lyapunov Function (BLF) with finite barrier properties for output-constrained control of nonlinear dynamical systems. Unlike conventional BLFs employing logarithmic or tangent functions, in which their barrier values diverge to infinity near constraints, the proposed approach ensures bounded barrier magnitudes as states approach prescribed limits, supported by rigorous theoretical proofs. To enhance robustness against disturbances, the design integrates a barrier function into an augmented error framework derived from sliding mode control principles. A quadratic cost formulation of this augmented error is adopted as the control Lyapunov function, enabling the synthesis of a robust controller that strictly enforces output constraints. Simulations on a four-dimensional nonlinear system validate the method's efficacy, demonstrating superior performance in constraint adherence and disturbance rejection compared to existing techniques.

Keywords: Nonlinear Control, Intelligent Control
Abstract: In order to enhance the precision of driving style recognition, this paper initially employs information entropy to divide dynamic windows. The driver's seven explicit features are transformed into high-dimensional deep features, which are through an entropy-guided attention mechanism and an LSTM network. Secondly, the KL divergence and the k-means algorithm embedded with the Gray Wolf Optimizer(K-GWO) are employed to directly cluster the high-dimensional features. Subsequently, the five categories of labels are designated as explicit latent variables and entered into the Information-Theoretic Generative Adversarial Imitation Learning (InfoGAIL) model in conjunction with the driving sequences. Then, probabilities of different driving behavior styles from the strategy network obtained from the model, together with the style ratios from K-GWO, are input into the Bayesian inference model to facilitate online recognition of driving styles. The method proposed in this paper improves the generalization, interpretability, and real-time performance of the model while increasing the accuracy of driving style recognition.

Keywords: Adaptive and Optimal Control
Abstract: The rapid development of cyber-physical systems (CPS) enhances the real-time sensing and dynamic optimization of eco-driving cruise control for connected automated vehicles (CAVs) in mixed traffic. However, since CPS is highly vulnerable to cyber attacks, false data injection (FDI), a typical form of attack, poses a significant challenge to eco-driving speed tracking. This paper proposes a radial basis function neural network (RBFNN)-based nonsingular fast terminal sliding mode control (NFTSMC) strategy for eco-driving speed tracking of vehicles under FDI attack. The strategy utilizes the RBFNN to estimate the lumped disturbance involving unknown FDI attack signals. Based on this, the NFTSMC enables the system state to converge to the sliding mode surface in a finite time, effectively suppressing chattering to achieve better eco-driving cruise control performance in the face of the FDI attack. The effectiveness verification is conducted on the SUMO-MATLAB/Simulink co-simulation platform by comparing it with the terminal sliding mode control under different driving cycles. The results show that the RBFNN-NFTSMC strategy can ensure the accuracy of speed tracking when FDI attacks in mixed traffic.

Keywords: Adaptive and Optimal Control, Robust Control
Abstract: In response to fossil fuel depletion and the need to reduce CO₂ emissions, distributed power sources such as solar power have gained attention. Compared to centralized systems, distributed generation offers benefits like reduced transmission losses and emergency power availability. To use such systems, sine wave inverters are required to convert DC to AC power, and control systems must ensure robustness to load variations, accurate sinusoidal tracking, and harmonic suppression. This study addresses harmonic distortion, which is particularly critical when nonlinear loads are connected or when downsizing filters. We apply a machine learning-based control approach using the Phase Compensated LMS (PCLMS) adaptive filter to suppress periodic disturbances and enhance inverter performance under practical constraints.

Keywords: Process Automation, Identification and Estimation
Abstract: First order and second order gradient-descent algorithms (Such as AdaGrad, AdaDelta, RMSProp, and Adam) have been widely used as the optimizer in deep learning methods. However, the calculation time of the mentioned algorithms is large especially when the task is difficult. In this paper, a fast deterministic gradient-descent learning-based time-series Transformer predictor proposed by C.Gan, W.H. Cao, and K.Z. Liu (TT-GCL) is designed, and it is introduced for rate of penetration (ROP) prediction in the drilling process. Firstly, Transformer, a popular deep learning method is set as the backbone predictor for time-series prediction. After that, the optimizer of the mentioned backbone predictor is replaced with a fast deterministic gradient-descent algorithm, which makes the proposed method have good optimization performance and fast learning speed. Finally, industrial application results of drilling ROP prediction demonstrate the effectiveness of the TT-GCL.

Keywords: Signal and/or Image Processing, Manufacturing Systems, Simulation of Large Systems
Abstract: Mechanical products are typically composed of multiple components of various sizes. The shape and weight of mechanical products must be optimized to achieve high strength with lightweight. However, machining errors are inevitable in the manufacturing process. By introducing the machining errors into topology optimization as shape deviations, robust design to the machining error can be achieved. In previous study, we proposed a method to generate shapes with random errors in 2D. In this study, the topology-optimized shape considering random errors was extended to three dimensions, and the measured data of the surface machined using NC wire electrical discharge machine was added to the part extended in this study. Therefore, the measured shape deviations caused by machining errors are incorporated into the ideal geometry. This enables more accurate reproduction of machining errors at the design stage, leading to improved quality prediction accuracy during manufacturing.

Keywords: Information Management Systems, Human Interfaces
Abstract: Even in the manufacturing industry, working on the condition monitoring have been progressing. The final goal of condition monitoring is not only to improve the factory equipment operating rates, but also to added value in engineering chain. In particular, there has been a movement to consider this as a total system that combines the on-site/physical space with the virtual space, collectively known as digital twin technology in recent years. This paper is shown the concept behind the condition monitoring.

Keywords: Robotic and Automation Systems, Computational Intelligence, Autonomous Decentralized Systems
Abstract: In recent years, generative artificial intelligence (AI) has become more advanced, and its applications have expanded rapidly. In this study, we applied multiple task-specific generative AI models to enable independent operation of an autonomous mobile robot. Our approach allows the system to understand a target action described in natural language, then plan a corresponding route, taking into account obstacles and avoiding collisions to reach the destination. We evaluated different generative AI models, each with a unique architecture, and examined how they performed in scenarios with varying levels of task abstraction. As a result of the evaluation, coordinating multiple generative AI models improved the system's ability to handle instructions with varying levels of abstraction.

Keywords: Components and Devices, Manufacturing Systems, Factory Automation
Abstract: To achieve smart manufacturing, it is essential to implement digital transformation that involves transmitting and utilizing the vast amount of information generated on-site to the cloud. Directly sending data collected from sensors to the cloud via the internet can lead to increased network load and high latency issues due to the sheer volume of data. Additionally, transmitting raw data without processing poses significant security risks. Therefore, edge computing technology, which allows on-site computers to rapidly process large volumes of data, perform data processing and analysis locally, and send only the minimal necessary analysis results to the cloud system, is gaining attention. Toshiba Corporation has developed the desktop type industrial computer FA3100TX model 800, which is suitable for edge computing.

Keywords: Sensors and Transducers, Safety, Environment and Eco-Systems, Components and Devices
Abstract: Nowadays, the incidence of heat stroke has been increasing, and the fall accident is the most common workplace accident. Under increasing the demand for work environment improvement, we have developed a wristband-type wearable sensor that assesses the risk of heat stress quantitatively and detects and issues alerts for fall accident from high height working site. By utilizing IoT management, we provide a good work environment and an accident response methods.

Keywords: Standard of Measurement, Innovative Systems Approach for Realizing Smarter World, Process Automation
Abstract: Thailand and Japan have been promoting economic cooperation for many years. Thailand has developed as a production base for Japanese companies but is trying to improve its international competitiveness in production technology and design. At this time, Thailand must engage in international standardization activities to have its technology recognized by the world. IEC TC65 Industrial-process measurement, control and automation is promoting this standardization. Japan is a major country in TC65 and has contributed a lot to the organization and standards development. On another hand, Thailand is a voting country, and although it does not yet have sufficient experience and human resources, it has ideas and enthusiasm. How can the two countries cooperate on international standardization in the automation sector? We will hold a Special Panel Session (SPS) on this issue. This paper describes the purpose and overview of the SPS.

Keywords: Nonlinear Control, Mechanical Systems Control, Adaptive and Optimal Control
Abstract: To pursue a reasonable control design methodology for nonlinear systems which are not globally stabilizable, this paper investigates the domain of attraction of the controlled upright equilibrium of an inverted pendulum on a cart. The true domain and its estimates computed with Lyapunov functions are compared. Since the Jacobian of the pendulum system is stabilizable, linear optimal controllers are extended to nonlinear ones which match the linear ones at the equilibrium. This paper introduces the concept of the largest achievable estimate of the domain of attraction independently of controllers, which is determined for each control Lyapunov function (CLF). Searching for a controller yielding a larger region of negative time-derivative of a Lyapunov function is not necessary. A CLF-based controller design always attains the largest estimate. Comparing it with backstepping-based and linear controllers, a discussion is given on how controller nonlinearity and Lyapunov functions change the estimate and true domain of attraction.

Keywords: Nonlinear Control, Adaptive and Optimal Control
Abstract: In this study, we address trajectory-tracking problems by proposing an error system that employs a local reference input. Its effectiveness is evaluated by using control-Lyapunov-function-based control, known as the pointwise min-norm control. A numerical example provides a concrete case where our proposed method improves control performance.

Keywords: Nonlinear Control
Abstract: This paper proposes an extended stable manifold method that is unified with the Stormer-Verlet method. The stable manifold method is an iterative numerical solver of Hamilton-Jacobi equations in nonlinear optimal control problems. The Stormer-Verlet method is a simple symplectic algorithm that can be applied to Hamiltonian systems. In this extension, the Stormer-Verlet method is used for improving a numerical integration of equivalent Hamiltonian systems of the Hamilton-Jacobi equations in the stable manifold method. The extended stable manifold method is applied to a nonlinear H infinity control problem to demonstrate its effectiveness.

Keywords: Nonlinear Control, Process Control, Transportation Systems
Abstract: In the present paper, we extend the Deficiency Zero Theorem (DZT) to delayed chemical reaction networks (CRNs) with generalized mass action kinetics, of which the dynamics of concentrations of species are described by the functional differential equations (FDEs). Here, the delay is described by a cumulative distribution function which includes discrete time delays. The extended DZT gives a sufficient condition, weak reversibility and zero deficiency, of CRNs so that the FDEs have an asymptotically stable equilibrium point in a positive stoichiometric compatibility class on the functional state space. This result is proven by using the Lyapunov functional, which is obtained by extending the functional for CRNs with mass action kinetics and discrete time delays.

Keywords: Nonlinear Control, Adaptive and Optimal Control, Mechanical Systems Control
Abstract: In this paper, we consider the trajectory tracking control of rigid body attitude subject to disturbance torque inputs. To achieve robust trajectory tracking, we employ the concept of input-to-state stability trajectory tracking control Lyapunov function (ISS-TCLF) and design a robust trajectory tracking controller based on the ISS-TCLF. We then introduce an adaptive disturbance estimator to mitigate the effect of disturbances more efficiently. The effectiveness of the proposed controllers is confirmed through numerical simulations.

Keywords: Mechanical Measurement, Sensors and Transducers, Identification and Estimation
Abstract: Non-destructive testing (NDT) using an ultrasonic transducer is a wide technique for measuring thickness of materials, including inspecting for material corrosion. The pulse-echo method is an acoustic pulse transmission to determine the time of flight (TOF), which is commonly used for distance measurement. The pulse propagation distance can be determined using the relationship between TOF and the speed of sound in the material. However, a conventional pulse-echo technique can detect background noise, which affects measurement accuracy. The M-sequence pulse compression technique generates by a pseudorandom sequence using a linear feedback shift register (LFSR), which enhances the cross-correlation properties of transmitted signals. Moreover, the M-sequence signal can be applied to improve the signal-to-noise ratio (SNR) and to reduce background noise. The M-sequence pulse compression technique achieves the best signal and ensures a stable measurement of the reflected signal. In this study, the different n-th order of the M-sequence signals on TOF deviation and material thickness determination, which is a near-field area of the transducers is proposed. The results of the M-sequence pulse compression technique are shown.

Keywords: Flow Measurement and Control, Signal and/or Image Processing, Opto-Electronic Measurement
Abstract: When the fluid flows around an object, viscosity induces shear stress on the surface, which is an important physical quantity in fields like aerospace, automotive, and laser processing. Oil-Film Interferometry (OFI) is a non-intrusive technique that visualizes and quantifies this shear stress. In this study, shear stress due to a jet impingement on a flat plate was measured using OFI. The spacing of the interference fringes was measured with respect to the elapsed time, revealing that shear stress decreases with distance from the jet impingement point.

Keywords: Sensors and Transducers, Mechanical Measurement, Standard of Measurement
Abstract: This study evaluates the span accuracy, dynamic response, and sensitivity of three MEMS pressure sensors recently used in infrasound (low-frequency sound) measurements. Pressure　variations from 1 Pa to 10 Pa were applied by vertically moving the sensors in atmospheric conditions. Two sensors accurately captured the pressure changes, while one failed to detect small variations due to high noise levels.

Keywords: Signal and/or Image Processing, Sensors and Transducers, Networked Sensor System
Abstract: This study is concerned with an advanced noise reduction approach for an infrasound observation system using a Micro-Electro-Mechanical Systems (MEMS) atmospheric pressure sensor. The infrasound is defined as atmospheric acoustic waves with frequencies lower than 20~Hz which are in an inaudible range of the human ear, and is excited by natural phenomena such as volcanic eruptions and tsunamis. In recent years, the MEMS atmospheric pressure sensor has been used for realizing multipoint observation of the infrasound. However, the noise ratio of the MEMS pressure sensor is higher than that of the dedicated sensor. In this study, we propose the noise reduction approach using a Finite-Impulse-Response type Zero-Phase Filter (FIR-ZPF) for visualizing clearly the infrasound. In the acceptable short time batch processing for updating the infrasound observation data, the FIR-ZPF is useful to the noise reduction with no phase delay. The efficacy of the proposed noise reduction approach is verified by the experiments with the lifting device for generating the artificial pressure variations. The three type MEMS atmospheric pressure sensors are installed into the lifting device. It was shown that the proposed approach using the FIR-ZPF enables to show precisely and clearly the trend of the atmospheric pressure sensor signal with suppressing the noise.

Keywords: Mechanical Systems Control, Modeling, System Identification and Estimation, Power Systems Control
Abstract: Backlash in the electric vehicle (EV) powertrain drive shaft causes unnecessary vibration and shocks during tip-in and tip-out maneuvers. This directly affects EV drivability and can even cause system failure. This article proposes a vibration suppression method using a backlash observer previously proposed by the authors. The method is then compared with various vibration suppression methods. Furthermore, the evaluation results show that the proposed method is highly effective for vibration suppression in EV powertrains.

Keywords: Transportation Systems, Mechanical Systems Control, Adaptive and Optimal Control
Abstract: Rapid electrification of vehicle powertrains has increased actuator degrees of freedom, and advances in external sensing now enable anticipation of road conditions. This paper presents a Model Predictive Control (MPC) approach that integrates split-(mu) road friction estimates from camera-based sensors into proactive steering control. In simulations where road friction asymmetry and driving force imbalance are introduced, the proposed method achieves reduced lateral deviation with less steering input. Additional validation using a driving simulator with a human driver confirms smoother transitions and improved controllability. These results highlight the potential of combining electrified actuators with sensor-driven anticipatory control for enhanced vehicle stability.

Keywords: Adaptive and Optimal Control, Multivariable Control
Abstract: In order to improve the exhaust gas purification performance of diesel engines, it is crucial to quickly transfer the temperature of the SCR (Selective Catalytic Reduction) system to its active temperature range. In this paper, we focus on the control system utilizing the engine exhaust as a heat source and derive a setpoint manipulation method which optimizes the transient performance under the system constraints. Simulations are conducted under various operating conditions and the effectiveness of the proposed method is discussed.

Keywords: Mechatronics Systems, Components and Devices
Abstract: This paper proposes a DC rotary motor with suppressed torque ripple. In general, DC rotary motors feature a simple structure and require less complex power electronics than their AC counterparts. However, conventional DC motors often suffer from torque ripple, which adversely affects control performance. To mitigate this issue, the proposed motor is designed to minimize torque ripple through optimization of the magnetic flux distribution. The validity of the design is confirmed through finite element analysis.

Keywords: Agricultural and Bio-Systems, Identification and Estimation, Network System Integration
Abstract: Controlling vapor pressure deficit (VPD) is essential for optimizing plant growth in sunlight-type plant factories. While previous studies have demonstrated the effectiveness of VPD control using fine mist systems, the spatial resolution of VPD monitoring has been limited. This study developed an IoT-based VPD measurement system with 27 sensors placed at different heights to capture three-dimensional environmental data. To evaluate the interpolation accuracy, the VPD values of the measured and estimated values were compared, and the mean squared error ranged from 0.000858 to 0.00142 kPa. The results show that variogram model parameters remained consistent during mist operation but varied significantly at night or under uncontrolled environments. These findings suggest that VPD control status and environmental conditions should be considered when applying spatial interpolation.

Keywords: Agricultural and Bio-Systems, Identification and Estimation, Network System Integration
Abstract: Vapor Pressure Deficit (VPD) has a large impact on plant yield and quality. Most studies on VPD control in plant factories have focused on measurement using a single representative value, and have not considered and controlled all VPD in a space. In this study, VPD values were interpolated by Ordinary Kriging from environmental sensors on 27 spatial points by spatial interpolation, and the accuracy was verified. The accuracy was also compared with Ordinary Kriging using sensor information from 9 points on a plane. The results showed an improvement of up to 0.0334 kPa (about 60.6 %) in RMSE (Root Mean Square Error) and 0.0255 kPa (about 60.1 %) in MAE (Mean Absolute Error). Therefore, the results indicate that extending the sensor installation from a flat surface to a spatial surface may improve the estimation accuracy.

Keywords: Agricultural and Bio-Systems
Abstract: This study presents the development of an IoT-based smart irrigation system enhanced by a Decision Tree (DT) model for precision water management in cannabis cultivation. The system utilizes environmental sensor data—temperature, humidity, and soil moisture—to automate irrigation decisions in real time, thereby reducing resource usage while maintaining optimal growing conditions. A lightweight DT classification model was trained using data collected from experienced farmers and deployed on a NodeMCU ESP32 microcontroller. The model achieved strong predictive performance (RMSE = 0.316, MAE = 0.100), demonstrating high alignment with actual farmer decisions. Comparative evaluations were conducted to assess water consumption, soil moisture levels, and crop productivity between the traditional irrigation method and the proposed DT-based system over a 120-day cultivation period. Results showed that the intelligent system reduced total water usage by 9.44% while maintaining comparable soil moisture conditions (1.75% difference). Moreover, a slight increase in final product weight (1.23%) was observed, indicating improved irrigation efficiency without compromising yield. The integration of machine learning and IoT into autonomous irrigation offers a practical, scalable, and sustainable approach to modern agriculture.

Keywords: Agricultural and Bio-Systems, Innovative Systems Approach for Realizing Smarter World, Intelligent Systems
Abstract: This research presents a cost-effective and adaptable Internet of Things (IoT) system for smart greenhouse management, integrating agricultural science and electronic engineering to upgrade traditional farming practices. The system identifies the need for environmental control across different planting areas, including indoor greenhouses and outdoor plots, within a single centralized infrastructure. Low-cost IoT devices built on the ESP8266-07 family, enabling both close-range Wi-Fi and remote Internet control. This system facilitates real-time monitoring and management of water valves, fertilizer water, fans, mist sprayers, and light bulbs. In addition, data from IoT devices were collected in an own built service machine (Server) and can be analyzed and illustrated in terms of comparative graphs with data from IoT devices that detect environmental factors, i.e., weather conditions, namely temperature, humidity, and light. This system provides a foundation for future integration of Artificial Intelligence of Things (AI-IoT) to enable predictive capabilities for yield optimization. For the field validation, the proposed system was setup and tested with 10 sets of IoT devices across 400-square-meters planting area, both inside and outside the greenhouses. Two 6x8 meters greenhouse with a height of 2.5 meters were deployed for controlling strawberry cultivation at Rajamangala University of Technology Lanna, Chiang Mai (Doi Saket).

Keywords: Agricultural and Bio-Systems, Innovative Systems Approach for Realizing Smarter World, Process Automation
Abstract: We propose a new plant growth monitoring and artificial pollination method utilizing drone downwash for smart strawberry management. Nevertheless, one of the major challenges in implementing this proposed method lies in effectively applying the proper downwash to target crops in constrained greenhouse environments. In this study, we conducted a series of experiments on the effects of drone downwash on crops under various conditions to address these challenges.　Our experimental results showed that the desired airflow velocity can be controlled by adjusting flight altitude, speed, and orientation even under constraints that prevent an optimal flight path. We also identified the factors that hinder the desired flight paths of drones in greenhouse environments. To explore effective downwash conditions for pollination, we conducted experiments to analyze flower vibration in response to airflow and the resulting pollen adhesion. Our experiments demonstrated that applying airflow at the timing of anther opening significantly improves pollen adhesion, while airflow patterns have minimal impact. The analysis also showed that flower vibration peaked shortly after airflow began, and further airflow had negligible additional effect.

Keywords: System Engineering, Intelligent Systems, Agricultural and Bio-Systems
Abstract: This paper presents a hybrid path planning system for orchard weeding robots that integrates U-shaped and Z-shaped patterns to generate a global path, ensuring effective coverage of weeds beneath fruit trees. The hybrid path combines the short traversal distance of the U-shaped strategy with the high coverage rate of the Z-shaped strategy. Local path planning, enabled by the Dynamic Window Approach (DWA), ensures real-time obstacle avoidance and smooth turning. Simulations in Gazebo validate the system, showing that the hybrid path reduces traversal distance compared to the Z-shaped path and improves coverage compared to the U-shaped path, while ensuring safe and efficient navigation in dynamic orchard environments.