Keywords: Optimization, Renewable Energy, Process control and instrumentation
Abstract: The problem of photovoltaic (PV) cell degradation can affect the shape of the I-V curve, which can lead to variations in the five parameters of the PV cell. This is the motivation behind the importance of knowing and extracting these parameters. The process starts by the measuring the output current and voltage (I-V curve) then applying a best fit to obtain the parameters. Both the noise of the instruments used for measurement and the size of the measured window can affect the accuracy of the obtained parameters. This paper presents a study about the effects of both the noise of instruments and the interval size. Varying the RMS of the noise of both current and voltage from 1 to 10%, the parameters are extracted from two case studies, first one starting the interval from the short circuit coordinates and the second one from the open circuit voltage, the size of the intervals are increased till reaching the whole curve.

Keywords: Optimization, Power systems, Transportation systems
Abstract: This paper presents an energy management strategy for electric vehicles (EVs) with a hybrid energy storage system (HESS), which combines a lithium-ion battery and a supercapacitor. The problem is formulated as a constrained multi objective optimization (MOO) that aims to: (i) match the vehicle’s average power demand, (ii) reduce battery thermal stress, and (iii) maintain the supercapacitor’s state of charge near its nominal level. The Particle Swarm Optimization (PSO) algorithm allocates power optimally between sources under thermal, dynamic, and state of charge (SOC) constraints. Simulation results from a 400-second driving cycle demonstrate that the proposed strategy lowers battery temperature by up to 9°C compared to a rule-based approach. Furthermore, energy sharing becomes more balanced, with the battery providing 65% of the power and the supercapacitor providing 35%, as opposed to the baseline approach, where the battery provides 95% and the supercapacitor provides 5%. This method improves energy efficiency, battery life, and thermal safety.

Keywords: Fault detection and Diagnostics, Image processing, Optimization
Abstract: Visual inspection of aircraft surface is one of the many steps in the maintenance routines. Usually performed by operators, this procedure might last days to be accomplished. The use of automated process can help reduce time and results in accurate detection of surface defects on aircraft, as they are vital to maintain structural soundness and flight safety. This paper proposes a deep learning framework for automated defect detection based on Faster R-CNN with ResNet-50 Feature Pyramid Network (FPN) as the backbone model. This model was trained and validated on a sizable, labeled aircraft images with a maximum F1-score of 0.555 achieved in the test set. This is the result of preliminary study, where the authors aimed to detect all types of defects without classification. To further enhance reliability and allow for human input, a custom annotation validation user interface was implemented via Python, which allowed aircraft inspectors to view, edit, add, and acknowledge predictions made by the model in an attempt to hold onto precise level of annotation. This system also facilitated the management of annotations, visualization on irregular aircraft zones, and the creation of reports thus allowing for inspection workflows. The results show that combining state-of-the-art object detection with domain expertise in validation as route to reliable semi-automatic, standards-compliant aircraft defect detection is plausible. Future work will involve expanding the dataset, tuning for accuracy, and incorporating human feedback for enhancement of model utility over time.

Keywords: Optimal control, Optimization, Autonomous Systems
Abstract: In this paper, a synchronous Integral Reinforcement Learning algorithm on an actor-critic structure is developed to learn online the solution to the optimal control problem for completely unknown input-constrained systems. The algorithm combines the advantages of two techniques. On the one hand, we employ the exploration framework by adding a probing noise into the system to both excite the system states, possibly guaranteeing the PE condition, and also make the algorithm completely model-free. On the other hand, the technique of experience replay is used to relax the PE condition. The stability of the proposed algorithm is shown via Lyapunov theory, while simulation results for a nonlinear system with open-loop limit cycle demonstrate the effectiveness of the algorithm.

Keywords: Optimization, Networks optimization, Autonomous Systems
Abstract: This paper presents a novel continuous-time distributed optimization algorithm that combines gradient tracking with prescribed performance consensus to solve constrained problems over undirected networks. The proposed method employs a barrier-function reformulation to handle local inequality constraints using only first-order information and integrates a dynamic consensus mechanism to track the global gradient while enforcing explicitly defined transient and steady-state bounds. This structure enables decoupled tuning of convergence rate and accuracy, enhancing both robustness and scalability. A Lyapunov-based analysis guarantees exponential convergence to the global optimum, while the communication scheme reduces overhead compared to existing continuous-time gradient tracking approaches. Simulation results on classification tasks and battery energy storage coordination demonstrate superior performance in convergence speed and steady-state error. They also validate the method’s applicability to constrained and fixed-time consensus scenarios, confirming its practical relevance for resource-constrained multi-agent systems.

Keywords: Estimations and identification
Abstract: The position of the center of gravity (CG) of a vehicle is an important parameter as it significantly affects vehicle loads distribution and vehicle dynamics. This article proposes an approach to estimate the longitudinal position of the CG from inertial measurements. The proposed approach does not require prior knowledge of vehicle parameters apart from vehicle wheelbase. The estimation procedure combines a direct estimation from the equations of motion with the state variable filter method used in the identification of continuous time model from sampled data. The filter is chosen based on general knowledge of vehicle dynamics such as the order of magnitude of characteristic frequencies of pitch motion. The estimates are obtained through a least squares method with instrumental variables. The algorithm can be implemented in both offline batch and online recursive forms, with low computational cost suitable for embedded applications. The method is tested and validated on a high-fidelity road vehicle simulation, with good results for different load distributions.

Keywords: Estimations and identification, Power systems, Transportation systems
Abstract: Energy management makes a significant contribution to the efficiency and reliability of modern systems, particularly in electric vehicles. Lithium-ion batteries, renowned for their high energy density and long life, are largely used as storage systems. Accurate state-of-charge (SoC) estimation is crucial to ensure performance and safety. This study compares three SoC estimation methods: the extended Kalman filter (EKF), the sliding mode observer (SMO) and the non-linear auto-regression neural network with exogenous inputs (NARX). The results show that the NARX model has the best accuracy, with a mean absolute percentage error of 0{.}0485, achieving notably higher accuracy than model-based approaches. This validates the robustness of machine learning techniques for battery state estimation.

Keywords: Estimations and identification, Observer design, Linear and nonlinear systems
Abstract: The aim of this paper is to provide fast state estimation for a class of linear systems where the measured output is corrupted by a non-zero mean noise. To this end, the generalize modulating functions method recently developed based on the observable canonical form is extended. First, as done in the existing works, the method is applied to obtain algebraic integral formulas for the state variables without needing the system's initial conditions. Then, in order to deal with the unknown bias, additional properties are introduced for the generalized modulating functions. Moreover, an integral formula is also provided for the bias based on a normalized classical modulating function. After showing the constructions of the required modulating functions, a numerical example is given to show the accuracy and robustness of the proposed method.

Keywords: Estimations and identification, Linear and nonlinear systems, Optimization
Abstract: In this paper, an improved KKL observer based on physics-informed neural network with modulating functions is proposed to quickly and robustly estimate the state of a nonlinear system in discrete noisy cases. Firstly, the original nonlinear system is transformed into a linear observer form. Secondly, in order to get fast convergence of the observer, we build a physics-informed neural network and train it over a small time interval to select an initial value for the observer. Additionally, to reduce the influence of noise in the loss function, we apply the modulating functions method to transform the observer form into an integral form without needing the initial condition. Finally, based on the learned initial value and the inverse of the transformation, the state estimation for the considered nonlinear system is realized. To demonstrate the efficiency and advantages of the proposed method, numerical simulation results are given.

Keywords: Fault detection and Diagnostics, Power systems, Renewable Energy
Abstract: The present paper introduces an explainable deep learning framework for the classification of voltage disturbances in microgrid AC buses. This framework employs Long Short-Term Memory (LSTM) networks in combination with SHapley Additive exPlanations (SHAP). The proposed model processes raw time-series voltage and current data to detect and classify 8 types of power quality events such as harmonics distortions, symmetrical dip and othes, without the need for manual feature extraction. The integration of SHAP values permits the extraction of interpretable information from the model’s predictions, thus ensuring the transparency of decision making processes. The experimental results demonstrate a high level of accuracy (F1-score = 0.99) for all categories and reveal the features that have the most significant impact on each disturbance type. A comparative analysis demonstrates that the proposed method surpasses existing techniques in terms of both classification performance and interpretability, making it suitable for realtime implementation in the context of smart grid monitoring and diagnostics.

Keywords: Linear and nonlinear systems, Control applications, Autonomous Systems
Abstract: Airborne Wind Energy (AWE) systems leverage tethered kites flying lemniscate trajectories to harvest high-altitude winds, offering an innovative solution for reducing fuel consumption and emissions in maritime transport. This paper provides a novel representation of the nonlinear kite's dynamics, based on the differential flatness property. In flat systems, all states and inputs can be expressed as functions of a suitably chosen set of outputs, referred to as flat outputs, and their derivatives. Through an explicit analytical derivation, an inverse dynamic model of the system is obtained, providing a foundation for the design of nonlinear feedforward control, path planning, and fault detection algorithms. These applications are particularly relevant for AWEs, where robustness and real-time performance are critical requirements.

Keywords: Linear and nonlinear systems, Discrete-continuous time design, Control algorithms implementation
Abstract: This work presents a control strategy based on a reinforcement learning algorithm for a linear system and compares it with the classical discrete–continuous control method. Both approaches were applied to a discrete system. Discrete–continuous control extends classical techniques by allowing the control signal to vary within the sampling interval, which improves accuracy; however, it requires knowledge of system parameters, limiting its applicability under uncertainty. As a modern and adaptive alternative, a data-driven method using the off-policy Q-learning algorithm is considered. This approach does not require a priori model identification or precise knowledge of the system parameters, as it learns directly from measured data. The developed control algorithm demonstrates robustness. Numerical simulations were carried out on an inverted pendulum system, confirming the effectiveness of both methods. In addition, an experiment was conducted to evaluate the impact of noise on the model. A comparative analysis of the two algorithms is presented based on system stabilization time and the Euclidean norm of the control vector.

Keywords: Linear and nonlinear systems, Stochastic control, Robust control and Hinfty control
Abstract: In this paper, the linear discrete time-invariant system describing the repetitive process is considered. The disturbance of the system belongs to a standard set of white noises, and the output of the system forms a sequence of random vectors, the anisotropy of which is derived. The anisotropy of the output consists of two terms: the first can be associated with the anisotropy of the random vector, and the second one relates to the mean anisotropy level.

Keywords: Linear and nonlinear systems, Modeling and simulation, Fault detection and Diagnostics
Abstract: This study addresses the modeling and design of a fault-tolerant controller for rail pressure in ship diesel engine injection systems using H_{infty} static output feedback (SOF) control. Disturbances are assumed to have known frequencies within a finite range. The objective is to maintain the rail pressure at its desired value by adjusting engine speed. A Takagi-Sugeno (T-S) fuzzy model captures the system’s nonlinear dynamics. Based on this model, a fuzzy fault-tolerant SOF controller is designed to ensure robust pressure regulation under actuator faults, formulated via linear matrix inequalities (LMIs). Simulation results demonstrate the effectiveness and robustness of the proposed approach.

Keywords: Fuzzy and neural systems, Linear and nonlinear systems, Time-delay systems
Abstract: This manuscript addresses the issues of asymptotic stability and stabilization in positive fractional-order Takagi–Sugeno fuzzy systems with fixed delays, employing a Linear Programming (LP) approach. The proposed results are derived using a Lyapunov–Krasovskii function and are further demonstrated through a numerical example.

Keywords: Fuzzy and neural systems, Linear and nonlinear systems, Time-delay systems
Abstract: This paper addresses two problems: the stabilization of delayed fuzzy fractional-order positive systems with bounded non-negative control, and the stabilization problem under non-symmetric bounds that are not restricted in sign. The obtained results are sufficient and are formulated in terms of Linear Programming (LP) conditions. A numerical example is provided to illustrate the effectiveness of the proposed theoretical results.

Keywords: Control algorithms implementation, Control applications, Robust control and Hinfty control
Abstract: In this paper, an asymptotic representation is obtained for the optimal anisotropic dynamical output feedback controller for linear discrete time-invariant systems and for the anisotropic norm of the closed-loop system with such a controller. Also, the maximum value of the mean anisotropy of the external disturbance, at which the optimal anisotropic controller is approximated by the H2-optimal controller with a given accuracy, is established. The numerical example of optimal anisotropic control problem solution for the linear discrete time-invariant system is illustrated by the graphs.

Keywords: Control algorithms implementation, Modeling of complex systems, Linear and nonlinear systems
Abstract: Transmission Control Protocol (TCP) continues to be the dominant transport protocol on the Internet. The stability of fluid models has been a key consideration in the design and performance evaluation of TCP and queue management systems. In this paper, we derive sufficient conditions for the local stability of a generalized TCP algorithm in the presence of heterogeneous round-trip delays across one, two, and many bottleneck links. A critical aspect is the scalability of the design considerations, as the systems scale from single to multi-bottleneck networks. Within the generalized model, we consider three specific variants of TCP: TCP Reno, Compound TCP, and Scalable TCP, along with intermediate and small buffer regimes, with Drop-tail queues. We derive scalable sufficient conditions with both buffer regimes. However, the small buffer regime has the advantage that it offers decentralized sufficient conditions where local stability can also be ensured. In a small buffer regime, TCP algorithms that follow our design considerations can provide stable operation irrespective of the number of bottleneck links or feedback delays in the network.

Keywords: Linear and nonlinear systems, Control applications, Modeling and simulation
Abstract: A Micro-Electro-Mechanical System (MEMS) gyroscope is employed in many real-time applications. However, these systems are sensitive to measurement errors and exhibit high nonlinearity. This paper deals with the constant faults of the position-dependent measurements in MEMS gyroscopes. In the dynamic model of the MEMS gyroscope, it is assumed that there are position-dependent errors in the actuation and sensing axes. To overcome this problem, a robust adaptive backstepping controller with integral action is developed for the control of the MEMS gyroscope. The asymptotic stability of the closed-loop dynamics is proved in the sense of Lyapunov theory. The viability of the proposed control method is tested by numerical simulations to show the robustness against position-dependent errors. The simulation results are presented with a comparison to the classical backstepping controller, and with different initial conditions. The obtained results show that the proposed controller method is robust to the constant faults of the position-dependent measurements in MEMS gyroscopes.

Keywords: Control algorithms implementation, Control applications, Power systems
Abstract: In this paper, we present an optimized Active Disturbance Rejection Controller (ADRC) for regulating the output voltage of a standalone Doubly Fed Induction Generator (DFIG)- based Wind Turbine (WT). The standalone system is connected to a nonlinear load and is subject to wind speed variations. A Genetic Algorithm (GA) is employed to optimize the ADRC parameters in order to minimize tracking errors and overshoot, while also reducing the power consumption associated with DFIG current and voltage. The DFIG and turbine models were developed in MATLAB/Simulink. Both simulation and experimental results demonstrate that the proposed GA-tuned ADRC outperforms the conventional controller.

Keywords: Control of telecommunications systems
Abstract: This paper presents a multi-band extension of the Digital PreDistortion (DPD) based on the Direct Learning Architecture and pruning structure. While DPD is widely used to linearize RF power amplifiers by acting as an inverse dynamic function, conventional implementations are generally restricted to single-band transmissions. This limitation constrains the spectral efficiency and capacity of modern telecommunication networks. In this work, we propose an adaptation of the Direct Learning Architecture to a multi-input/multi-output (MIMO) DPD framework to compensate both inter-band and inter-carrier intermodulation in multi-band RF system. Particular attention is given to the Generalized Memory Polynomial (GMP) model, selected for its high accuracy and modeling precision. We also propose a reformulation of the doubly orthogonal matching pursuit approach to select the active terms of the GMP model in each band, while integrating the DLA approach to enhance linearization performance. The effectiveness of the proposed linearization approach is validated through simulations using a dual-band nonlinear RF transmitter designed for LTE/5G sub-6G signals.

Keywords: Fractional order systems, Linear and nonlinear systems, Control applications
Abstract: This research work primarily focuses to establish the sufficient conditions for the existence and uniqueness of mild solution and trajectory controllability results for a class of the nonlinear fractional neutral-type integro-differential delayed system. The proposed system is studied in a Hilbert space, with nonlocal conditions and governed by the Caputo conformable fractional derivative. A key advantage of using this derivative is that it possesses several classical properties, including quotient rule, Rolle's theorem, product rule, mean value theorem, and linearity, distinguishing it from traditional fractional derivatives. Firstly, the proposed stochastic control problem is transferred into an equivalent fixed-point problem using the Riemann–Liouville conformable fractional integral operator. Then, the existence and uniqueness of mild solution are established by applying tools from fractional calculus, Laplace transform techniques, and semigroup theory for bounded linear operators and the Banach fixed-point theorem. Finally, Grönwall's inequality is used to investigate the trajectory controllability of the system. At the end of the paper, a concrete example is provided to validate the abstract results.

Keywords: Modeling and simulation, Linear and nonlinear systems, Image processing
Abstract: This paper focuses on the mathematical modeling of a bench-quadcopter system. We present an approach for evaluating the viscous friction coefficient in the test bench's hinge joint. For the considered test bench-quadcopter system model, we have synthesized a covariance controller that stabilizes the system in its top unstable equilibrium position. A dataset was annotated for Keypoint Detection tasks, and a YOLOv8 model was trained and tested for HPE to establish the relationship between thrust forces generated by UAV motors and PWM signals. The synthesized algorithms were tested, and implementation results on a physical system are presented in the final section of this work.

Keywords: Image processing, Industrial control
Abstract: Abstract—In aviation, maintaining structural integrity is crucial to maintaining aviation safety and operational security. Surface wear, corrosion, and cracks are typical structural defects that can seriously compromise components for aircraft. Employing innovative image processing techniques this study provides a comprehensive approach to support the creation of systems that enable the automatic recognition and classification of these findings. The primary objective is to develop a verified image-based database that improves maintenance processes and inspection performance. The process basis is a structured finding catalogue that was created after an extensive examination of scientific and industrial sources. This catalogue standardises terminology and makes it easier to manually annotate and classify defects consistently. A rigorous pipeline that includes image collection from various sources, data augmentation to improve generalisation, manual annotation based on the catalogue, and expert validation to guarantee accuracy and consistency is used to build the dataset. A crucial component of this initiative is the Aircraft Inspection Repository. By acting as a centralised platform that improves data accessibility, expedites maintenance workflows, and guarantees regulatory compliance, it is intended to address the challenges of gathering, monitoring, and analysing inspection data. The repository greatly improves maintenance planning and decision-making by arranging inspection records across various aircraft models, providing dynamic data analysis tools, and enabling collaborative access to findings.

Keywords: Signal processing
Abstract: This paper evaluates and compares the performance of the MUSIC (Multiple Signal Classification), CMSR (Covariance Matrix Sparse Representation), and NC-CMSR (Non-Circular Covariance Matrix Sparse Representation) methods under complex conditions. We analyze their behavior in scenarios characterized by a limited number of sensors, highly noisy environments, and with a small number of snapshots. The study focuses, particularly, on the evolution of the Root Mean Square Error (RMSE) for direction-of-arrival (DOA) estimation as a function of the signal-to-noise ratio (SNR) and the number of snapshots, as well as on the detection probability with respect to SNR. The results highlight the relative robustness of the CMSR and NC-CMSR approaches under these constrained conditions, while also emphasizing the benefit of tailored processing for non-circular signals.

Keywords: Robotics, Optimal control, Modeling and simulation
Abstract: This paper addresses the robust trajectory track- ing control of a Delta parallel robot using two discrete- time control strategies: Discrete Linear Quadratic Regulator (DLQR) and Sliding Mode Control (SMC). The nonlinear dynamic model of the robot is linearized around an equi- librium point, and both controllers are designed in discrete- time for real-time implementation. The DLQR aims to ensure optimal tracking with minimal control effort, while the SMC enhances robustness against model uncertainties and external disturbances. The performance of both methods is evaluated and compared through MATLAB/Simulink simulations using sinusoidal reference trajectories. Results demonstrate that both controllers achieve accurate tracking, with the SMC offering superior robustness at the cost of potential chattering, and the DLQR providing smoother control signals. This study highlights the strengths and trade-offs of each method for embedded robotic applications.

Keywords: Estimations and identification, Power systems
Abstract: This paper presents a short-term electricity demand forecasting model for the Duques de Soria campus in Spain, using Artificial Intelleigence including Random Forest (RF), Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM) networks. Based on six years of hourly records from a smart meter, the study explores the impact of various features such as lagged load, temporal variables, calendar indicators, and weather conditions on prediction performance. After thorough data cleaning, feature engineering, and model optimization, the RF model, which utilized only historical load and hour features, achieved the best results with an MAPE of 9.51% and an MAE of 5.95 %. While MLP showed strong robustness results with 9.66 % MAPE with the same scenarios, LSTM required richer sequences and more training data to perform better. The study concludes that careful feature selection is just as crucial as algorithm choice, and that temporal features at the hourly scale provide the highest predictive performance. This work supports sustainable energy management on university campuses by enabling more accurate, data-driven forecasting.

Keywords: Control applications, Robust control and Hinfty control, Linear and nonlinear systems
Abstract: This paper investigates whether increased model fidelity enhances tracking performance in the context of the Quanser three-degree-of-freedom (3DoF) Hover system. A two-layer control strategy is implemented to assess the impact of system modelling accuracy on the effectiveness of model-based controllers. Two dynamic models of the 3DoF Hover are employed, a commonly utilised nonlinear model and an improved version incorporating frictional and gravitational effects, which has been previously shown to better capture the open-loop dynamics. These models are embedded within a control framework that integrates feedforward control with linear parameter-varying (LPV) feedback to ensure accurate trajectory tracking and platform stability. The feedforward controllers are individually derived from each model to generate reference control inputs that directly counteract system dynamics. To mitigate unmodeled dynamics and correct tracking errors, an LPV error-feedback controller is introduced. For its design, the system model is reformulated as a state-dependent convex combination within a polytopic representation, enabling the derivation of sufficient conditions in the form of linear matrix inequalities (LMIs). The proposed control strategy is validated through real-time experiments, demonstrating its practicality and improved tracking accuracy, especially for the improved model.

Keywords: Control applications, Modeling and simulation, Motion control
Abstract: This paper introduces an extension of the classical wave-based control (WBC) approach that eliminates the influence of external forces on the control of mechanical chains. While the conventional method, referred to as WBC-A (wave-based absolute control), relies on absolute coordinates and suffers from undesired position shifts under external disturbances, the newly proposed method, WBC-R (wave-based relative control), employs relative displacements between masses. The core innovation lies in the introduction of a spring with controlled preload inserted between the first two elements of the chain, where preload regulation is governed by relative mass positions in conjunction with the standard WBC framework. The effectiveness of the proposed approach is demonstrated through simulation studies, confirming its capability to maintain the desired system position independently of external forces.

Keywords: Control applications, Power systems, Optimization
Abstract: This study proposes a state feedback-based controller design for Load Frequency Control (LFC) in isolated power systems employing a non-reheated turbine. The controller architecture integrates a proportional-integral (PI) control structure, with its tuning parameters analytically derived using standard forms, particularly based on the ISTE and IST2E performance indices. The method allows direct computation of the PI controller gains without requiring iterative optimization procedures, thereby simplifying the design process. Comprehensive simulations reveal the effect of standard form parameters on the system’s dynamic performance and demonstrate that the proposed method enhances both transient and steady-state responses under varying operating conditions. Compared to conventional PI-PD and PID controllers, the proposed ISTE and IST²E methods achieve up to 38% improvement in settling time and a 72% reduction in peak value over PI-PD, and 52% improvement in settling time and 92% reduction in peak value over PID, respectively. These results highlight the practical advantages of the standard form approach, particularly in achieving robust and efficient LFC.

Keywords: Control applications, Real time systems, Modeling and simulation
Abstract: This paper presents the design and evaluation of a polynomial-based Generalized Predictive Control (GPC) applied to a two-rotor aerodynamic system (TRAS). After identifying separate ARMAX models for the vertical and horizontal axes using PRBS excitations, a multi-step predictor is formulated via two Diophantine equations and expressed in matrix form to define a quadratic cost that balances setpoint tracking and control smoothing. The analytical solution yields an RST controller implementable in a receding horizon fashion. Simulation studies in isolated vertical and horizontal planes investigate the influence of prediction horizon, control horizon, and weighting parameter (lambda), highlighting the trade-off between aggressiveness and transient stability. Simultaneous two-axis control demonstrates precise tracking without unwanted coupling. Results confirm the robustness and flexibility of the GPC for this type of process, pointing to future robust and constrained extensions.

Keywords: Discrete-continuous time design, Modeling of complex systems
Abstract: The increasing complexity and inter connectivity of smart grids require modeling approaches that can effectively capture their dynamic, event-driven, and distributed nature. In this context, modeling the smart grid as a Networked Discrete Event System (NDES) offers a powerful framework for analyzing and controlling its behavior. This approach represents the components of the smart grid such as smart meters, distributed energy resources, and energy management systems as interacting automata that communicate through discrete events and control signals.

Keywords: Control applications, Industrial control, Multivariable control
Abstract: This paper presents an implementation of an implicit Generalized Predictive Control (GPC) strategy applied to a wastewater treatment plant (WWTP) modeled via the SimBio simulator. Without identifying an explicit dynamic model, the control law is derived directly from system input-output behavior. The main goal is to regulate the effluent ammonium (NH_4^+) concentration by manipulating the aeration rate, while coping with perturbations such as influent flow and ammonium concentration. Several configurations of the control parameters were tested and evaluated using process performance indicators. The obtained results show that the proposed controller allows a good tracking of the desired set-point while presenting a low energy consumption during the considered period of the experiment.