Keywords: Control Applications, Nonlinear Systems, System Identification and Modeling
Abstract: This paper presents a controller based on input-output linearisation (IOL) for a vapor compression cycle leveraging a moving-boundary model of the evaporator. After a presentation of the test rig and adaptations of a previously published high-order simulation model, the control-oriented moving-boundary model is derived. Special emphasis is laid on steady-state solutions of the governing partial differential equations. The corresponding ansatz functions enable a high model accuracy while keeping the model order low. The IOL based on the moving-boundary model is compared by simulations with a previously published IOL based on a lumped-parameter approach. Here, the IOL based on the moving-boundary model shows a high performance in a much larger operation range. These results can be confirmed through the comparative evaluation with experimental data. Finally, we show the general applicability of the controller with an implementation on a dedicated test rig at the Chair of Mechatronics, University of Rostock.

Keywords: Control Applications, Predictive Control, Adaptive and Robust Control
Abstract: This work presents the estimation and control stages of an induction motor. Starting from classical field orientation control scheme, in our approach the unknown state of the system is estimated based on the reduced-order observer, and for control of output variable (the speed rotor) was used two controllers: a robust adaptive controller, and a multiple linear predictive controller, obtained by using linear approximation of the nonlinear model of the considered system. The validity of the proposed estimation and control scheme is illustrated by performed several tests in a simulation environment.

Keywords: Control Applications, Autonomous Systems, Multi - Agents Systems
Abstract: Autonomous Mobile Robots (AMRs) are essential in various industries due to their efficacy and their usefulness in securing the transportation of goods. These robots autonomously execute tasks in intra-logistics operations, spanning manufacturing, warehousing, and healthcare settings. Moreover, AMRs can interact with humans as collaborative assistants. This paper proposes a longitudinal coalitional control (CC) approach for an interconnected AMRs group. The CC method ensures that the AMRs are controlled so that, both position and velocity errors are minimised, thereby maintaining the shape of the group. Additionally, the method allows for dynamic communication topology switching by enabling or disabling communication links to reduce communication usage. The results show that the coalitional algorithm with switching topologies yields a better cost performance compared to individual topologies, proving the efficacy of the CC method in achieving satisfactory performances with minimal communication usage

Keywords: Control Applications, Optimization and Optimal Control, Fault Diagnosis and Fault Tolerant Control
Abstract: Di-Cumyl-Peroxide (DCP) is a crucial cross-linker extensively utilized in the polymer and chemical industries. An industrial reactor is employed to decompose DCP under adiabatic conditions. DCP is a widely recognized exemplar of a runaway phenomenon in which thermo-kinetics of multiple degrees involving Arrhenius temperature dependency get entailed. Chemical analysis reveals that effective process parameters as well as a few other external factors have a substantial impact on the decomposition process. To address these challenges, a cost function is developed using the CasADi optimization framework. This function is then integrated with a standard nonlinear model-predictive control law to effectively manage unmeasured perturbations and process model mismatches in the batch reactor. Additionally, a cubature Kalman filter was employed to exploit accessible state detectors and accurately predict uncertainties associated with the reactor. The effectiveness and feasibility of the fault-tolerant NMPC controller were evaluated by examining key parameters such as the cooling period, control aggression, and observer accuracy.

Keywords: Control Applications, Variable - Structure/Sliding - Mode Control, System Identification and Modeling
Abstract: A permanent magnet synchronous machine (PMSM) is considered in this paper in the presence of inductance saturation. The cascaded control structure consists of an inner and outer control loop. In the inner loop, a decoupling structure is implemented. Two sliding mode controls (SMCs) are employed to robustify the current decoupling control. The combination of a decoupling controller (DC) and a SMC allows to obtain not only a desired sliding dynamics inside non-interacting controlled invariants but simultaneously a desired reaching phase dynamics that is characterized by the external dynamics of the subspaces. The decoupling approach allows to obtain a straightforward dynamics to be controlled. The presence of saturation, however, requires the identification of the quadrature inductance L_q. Otherwise, due to the huge impact of the current i_q on this inductance, the control performance would decline. An identification of this variable allows to adapt the control structure properly. The identification is realized using an extended Kalman filter (EKF), where the maximum torque per Ampere (MTPA), SMC and DC are adapted using the estimated L_q values. The reference currents for the SMC controllers are determined by a MTPA strategy for the outer control loop. Here, the reference torque is calculated by the combination of geometric control and SMC. Improvements in the performance of the controller are pointed out by dedicated computer simulations.

Keywords: Control Applications, Mechatronics, Nonlinear Systems
Abstract: This paper presents the control of a linearized Maglev system, which is obtained using the Isidori feedback linearization method. In this system, the gravitational force is considered as a disturbance, and strictly speaking its presence prevents the application of this methodology since it cannot be decoupled through pure feedback linearization. In fact, the relative degree of the disturbance with respect to the position is less than the relative degree of the input with respect to the position. For this reason, an additive impulsive action is used for approximative cancellation of this constant. The linearized system is then controlled using a PIDD^2, through the classical root-locus method, and the results are evaluated based on simulation studies. For the utilization of state variables and system parameters in the control laws, an extended Kalman filter is employed within the loop, rendering this an advanced modelbased feedback control strategy.

Keywords: Data - Driven Control, Stochastic Systems, Predictive Control
Abstract: Emulsification processes show a plethora of use cases in different industries. The complexity and intransparency of many emulsion systems make it hard to apply classic control approaches. The operation of these systems therefore often diverts towards a manual open-loop control. While population balance models (PBM) have been explored for multiple decades, they are rarely used in practice for closed-loop control due to the high computational effort. For this purpose a data-driven modeling approach specifically tailored to the control of complex emulsification devices is introduced. A new simplified description scheme of particle size distributions in combination with Gaussian process regression on a reasonably sized dataset can predict the system change. It additionally gives a useful measure of uncertainty for the predicted change, which is propagated onto the discrete distribution description. The concept is proven with leave-one-out cross-validation, before showing its potential in a model predictive control (MPC) simulation.

Keywords: Data - Driven Control, Control Applications, Nonlinear Systems
Abstract: Building on the continued focus on the control performance of the Permanent Magnet Synchronous Motor (PMSM) sensorless control systems based on a Field Oriented Control (FOC) type strategy, this paper shows a control structure using a Super Twisting-Terminal Sliding Mode Control (ST-SMC) type control, and in terms of estimating the speed of the PMSM rotor, the performance of an Sliding Mode Observer (SMO) type observer is improved by using a Phase Locked Loop (PLL) control loop. The performance of the sensorless control system is improved by optimally tuning the parameters of the ST-SMC type controller using an Ant Colony Optimisation (ACO) computational intelligence type algorithm. Control structures, observer and controller synthesis and demonstration of their convergence are also presented. The numerical simulations carried out in the Matlab/Simulink demonstrate the superiority of the proposed sensorless PMSM control system compared to the classical version, and the quality indicators used are: settling time, speed signal ripple, stationary error, and Total Harmonic Distortion (THD).

Keywords: Data - Driven Control, Control Applications, Nonlinear Systems
Abstract: Starting from the fact that the Field Oriented Control (FOC) control strategy of the permanent magnet synchronous motor (PMSM) gives good results in terms of the usual indicators of response time quality, overshoot, steady-state error, but only in the usual operating range of the PMSM. The Maximum Torque Per Ampere (MTPA) control strategy is used to limit current during operation while maintaining the required load torque, while the Field Weakening (FW) control strategy is used to achieve speeds above the rated speed of the PMSM. These strategies are also complemented by the use of a Model Reference Adaptive System (MRAS) observer for PMSM rotor speed estimation and a Butterfly Optimisation Algorithm (BOA) for obtaining optimal values of the tuning parameters of the PI speed controller. The PMSM operating equations and the FOC, MTPA and FW control strategies are also presented, together with the implementation structures in the Matlab/Simulink environment. The performance of these PMSM control systems in the corresponding operating regimes is presented through numerical simulations performed in Matlab/Simulink.

Keywords: Data - Driven Control, Linear Systems
Abstract: The purpose of this paper is to propose a novel perspective, based on Willems’ “behavior theory”, on the design of an unknown-input observer for a given linear time-invariant discrete-time state-space model, with unknown disturbances affecting both the state and the output equations. The problem is first addressed assuming that the original system model is known, and later assuming that the model is unknown but historical data satisfying a certain assumption are available. In both cases, fundamental concepts in behavior theory as the projection of a behavior, the inclusion of a behavior in another one, and the use of kernel and image representations, provide quite powerful tools to determine necessary and sufficient conditions for the existence of a UIO, as well as algorithms to design one of them, if it exists.

Keywords: Linear Systems, Adaptive and Robust Control, Uncertain Systems
Abstract: Singularly perturbed systems (SPSs) are a class of processes with dynamics naturally viewed through multiple time scales. The classical paradigm is to separate them into their associated reduced-order (slow) and boundary layer (fast) subsystems for any subsequent analysis. In opposition to this, the Robust Control Framework (RCF) implies a different system structure with added design flexibility. With this in mind, the scope of the current paper is two-fold: on one hand, to propose a technical adaptation such that an SPS can be rewritten for the RCF without adding conservativeness, with the perturbation parameters seen as uncertainties, and on the other hand, to provide means for a corresponding discrete-time regulator to ensure the performance metrics specified through the RCF. We consider the extended formulation of three-time scale linear time-invariant (LTI) systems, which can be further particularized to two-time scales only. The proposed solution is presented in an end-to-end manner in a numerical case study.

Keywords: Linear Systems, Fault Diagnosis and Fault Tolerant Control
Abstract: This paper proposes a fault tolerant control strategy for discrete-time descriptor systems using a reconfiguration-based technique. The proposed approach is designed to achieve fault-tolerance capabilities without modifying the nominal controller of the descriptor system but adding a reconfiguration block including a virtual sensor and a virtual actuator. The implementation of the virtual actuator and virtual sensor is based on the system input-to-state stability criterion through linear matrix inequalities. Finally, the effectiveness of the proposed technique is illustrated on an academic simulation example.

Keywords: Robotics
Abstract: Modeling obstacles inside the operating scene of a mobile robot is a necessary operation to finally plan a desired trajectory of movement for the robot between an initial and an end position. The process of modeling the operating scene, considered here with fixed obstacles of rigid solid type, is laborious, and can be achieved by several solutions. In the end, each obstacle or group of obstacles is included in a model that will be important for avoiding unwanted collisions during the movements of the mobile robot. This paper develops a method of polygonal mod-eling of obstacles by sets of nodes which belong a normalized discretized grid. From a theoretical point of view, this paper includes the general algorithm of this method, solutions for local filtering of nodes involved in the modeling process, and solving some especial situations which can appear in the ar-rangement of nodes. The paper also includes a case study on the application of the theoretical aspects presented, both the algorithm and filtering solutions, for highlighting the aspects signaled. The goal of the algorithms is to build flat polygonal models for obstacles, called here primary envelopes, which will later be used to plan the trajectory of the mobile robot inside of its operating scene.

Keywords: Robotics, Optimization and Optimal Control, Autonomous Systems
Abstract: This paper addresses the problem of planning the motion of a mobile robot that has to visit some points of interest, each point having a specific time interval in which it can be reached. The robot is omnidirectional and it evolves with constant speed in an environment cluttered with polygonal obstacles. In the case of no obstacles, the problem would be a Travelling Salesman Problem with Time Window (TSPTW) constraints. Our solution is innovative by enabling obstacles for TSPTW scenarios, and it accomplishes this by coupling visibility graphs, graph reduction and mathematical programming. The method first constructs a visibility graph based on the obstacles and points to be visited, and then it maps this graph to another graph whose nodes correspond only to robot's initial position and points of interest. Finally, a TSPTW problem is solved on the reduced graph by using a Mixed Integer Linear Programming (MILP) mathematical formulation, and the solution is projected to the initial environment. The solution development is accompanied by illustrative numerical simulations.

Keywords: Robotics, Control Applications, Computer Vision
Abstract: This work is set to design pose-free visual feedback control laws. Our contribution is linked to improving 3D pose-free visual servoing strategy using 2D predictions of visual features' trajectories. Given a set rigid body characterized by a set of 3D point features, our proposed visual servoing strategy enhanced with predictive elements is based on computing the following sample time positions of the point features given the known camera velocity from the previous time moment. In this way, the architecture decides to discard the point feature before exiting the field of view. Thus, any unnecessary computations are removed, and the camera's motion is ensured to be more natural. The simulation results emphasize the potential of the new visual control architecture.

Keywords: Robotics, Predictive Control, Control Systems Design
Abstract: Cable-Driven Parallel Robots (CDPRs) are a type of parallel robots that uses cables to position a moving platform inside its workspace. The cables offer many advantages from an operational point of view, from increasing the size of the workspace to handling heavy loads. However, they raise an issue in modeling their complex physical behavior. Simplifying hypotheses are usually made to facilitate the incorporation of a model in the control law in certain operational conditions, such as straight and massless cables. In this work, a Quasi-Linear Model Predictive Control (QLMPC) is presented based on a flexible cable model and is tested on some challenging scenarios. The cable model is based on the assumed mode approach. A comparison in simulation is made with a model predictive control based on a straight and inelastic cable for an eight-cables 3D suspended CDPR.

Keywords: Mechatronics, Robotics, Control Applications
Abstract: Object-grasping is one of the most common tasks in daily life. However, this task is very challenging for elderly human subjects with weak or dysfunctional hand. Therefore, hand assistive device is necessary to enhance the grasping performance. For the improvement in grasping assistance task, force control strategy along with exoskeleton’s design, is one of the major objectives in assistive technology. This study proposes a robust sliding mode force controller scheme for different-sized object-grasping tasks. To verify the efficacy of the proposed controller during object-grasping task, constant grasping experiment is performed on two elderly human subjects. Furthermore, we have incorporated real-time external disturbance rejection experiment to establish the robustness of the proposed force control scheme. The extensive grasping experiments involving human subjects show that the proposed robust force control strategy with the designed two-fingered exoskeleton can be preferable for performing object-gasping task in hand assistive technology.

Keywords: Human - Computer Interface, Software Methods and Tools, Robotics
Abstract: This paper focuses on developing a simulation platform that enhances Human-Robot Interaction through intuitive and safe interchanges between human operators and a UR5e robot by operating a Digital Twin via Mixed Reality. Using the ROS Noetic framework and Unity software for development, in conjunction with Microsoft HoloLens 2, this work presents a system based on Mixed Reality designed for collaborative robotic experiences. Moreover, a key innovation in this research is integrating a ZED camera for real-time human tracking, which enables the simultaneous simulation of humans and robots within a shared mixed environment. This advancement builds upon an existing digital twin of the robot. However, it also significantly enriches the system’s capabilities for ensuring safety and facilitating intuitive interactions, validating the implemented methodology through simulation and real-life experimental results. The paper examines the implications of this development in different industrial and research contexts. It highlights the growing significance of mixed-reality technologies in creating complex human-robot interaction systems that ensure safety and efficiency.

Keywords: Smart Cities/Houses/Grids
Abstract: This paper provides the role of Federated Learning (FL) in empowering smart cities, with an emphasis on technology, applications, future directions, approaches and strategies, and the FL environment as a part of this overview, through investigating and discussing the latest research on applying FL in a variety of fields in smart cities by using the literature review as a methodology in the paper. FL is a decentralized machine learning technique that addresses data collecting, privacy, and security issues. Additionally, FL offers potential solutions for problems in smart cities, including enhancing data privacy, real-time anomaly detection, resource optimization, environmental monitoring, and smart grid management. Moreover, the paper presents some applications and relevant work in the FL in smart cities fields and summarizes them to introduce the main limitations of their work and future directions.

Keywords: Cyber - Security, Distributed Systems, Web services and applications
Abstract: The real estate market, as an engine of economic growth, directly affects both financial stability and the development of communities at local and global levels. Nevertheless, it faces significant challenges, ranging from complex legislative processes and bureaucratic inefficiencies to fraud risks and high costs. Addressing these issues requires a paradigm shift to improve efficiency and transparency in this sector. The proposed solution aims to integrate emerging technologies such as blockchain and smart contracts to secure transactions and create a tamper-proof digital history. The implementation of dynamic non-fungible tokens (dNFTs) facilitates property transfers and digitally models real estate assets. A novel communication protocol and store policy are proposed for storing real estate information on the blockchain network and on decentralized storage so that the digital token representing the property can change based on external factors, e.g., change in ownership, actual updates being done on the property, or an update in valuation. The proposed framework ensures trustworthiness, transparency, traceability and security, while providing a flexible architecture with the main system components, including ways to model, update, list and buy a real estate property.

Keywords: Distributed Systems, Machine Learning
Abstract: Over the years, the concept of Federated Learning (FL) has gained popularity based on compliance with the General Data Protection Regulation (GDPR). This framework allows multiple participants to collaborate on training a shared global model, without exchanging training samples. In real-world applications, the data of different participants are usually non-independently and identically distributed (non-IID); in these cases, the clients are vulnerable to producing biased local models, for which FL-based aggregation could be improper. Several problems may also arise when some clients work with an insufficient amount of data or are exposed to potential attacks. To address these main issues, this paper proposes a new algorithm that evaluates the accuracy of models derived from participating clients and uses Bayesian regression to aggregate the local models. Through this strategy, the number of clients who can negatively affect the global model can be reduced, whether they have been attacked or have obtained poor performance for other reasons (e.g., an insufficient amount of data). The experiments are performed using a previously proposed algorithm, FedAccSize, together with this new extension, FedBayes. The considered scenarios use independent and identically distributed (IID) data, as well as non-IID data to demonstrate the ability of the proposed algorithms to obtain a safe aggregation of the global model. Based on the comparison with the established algorithms FedAvg and FedAvgM, it results that assessing the quality of the local models is crucial for aggregation.

Keywords: Distributed Systems, Communication Networks, Cyber - Security
Abstract: This paper presents a blockchain-based platform for managing and issuing academic certificates that features security, transparency, non-repudiation and traceability. Relying on the security provided by Ethereum blockchain and using asymmetrical cryptography in certificate generation, combined with certificate revocation mechanisms, the proposed platform offers a reliable, decentralized and secure solution that can be used by academic institutions to streamline the management of academic certificates and reduce bureaucracy.

Keywords: Distributed Systems, Software Methods and Tools, Computational Methods
Abstract: This paper presents the performance analysis and the most significant design decisions taken to improve the runtime performance of a quality-diversity volume rendering framework, focusing on using parallel and distributed computing to optimize specific stages within the framework pipeline. After considering the benefits and trade-offs of several aspects like minimization of execution time, portability and hardware independence, we developed a hybrid CPU-GPU parallel computing model that increased the overall efficiency. Using this model, we achieved significant improvement in runtime speed for critical components of the framework compared to the sequential implementation. Additionally, this model can be applied in a wide range of scenarios, where the performance improvements provided by GPU processing can be complemented by CPU parallelization, with better runtime results.

Keywords: Embedded Systems
Abstract: This paper presents a RISC-V extension, called RiscDaqExt designed for Digital Acquisition and Control over 32 individually managed digital channels. Each channel can be configured as input or output, inputs can be negated or filtered before being recorded using specific masks and output channels source can be chosen between values given with a mask, result of a multi-signal operation on specified inputs or a PWM signal generated by the SigWavy extension. The extension is also capable of measuring PWMs frequencies and duty cycles. The extension is controlled using a dedicated ISA extension containing instructions for setting/getting configuration/values and for controlling the storing records of 32 signals values with 32-bit timestamps to an attached DDR3 memory or directly to hosting PC’s RAM via Ethernet. The proposed solution, embedded into the RISC-V CPU named RiscDaq manages to initiate an Ethernet transmission of digital records 9.78x times faster than a MicroBlaze microcontroller, although our SoC operates with a 4x lower frequency, and outputs the result of logical operation performed over maximum 31 digital channels, with a constant 20 ns propagation delay, 49.2x times faster than a Cortex-M7 clocked at a 6x times higher frequency.