Pub Date : 2025-11-01DOI: 10.1016/j.ejcon.2025.101313
Frida Heskebeck , Pex Tufvesson , Ask Hällström
This study examines the effectiveness of LU-PZE3, a web-based interactive tool designed to help students visualize and interact with fundamental concepts in automatic control. A total of 200 students enrolled in an introductory course on automatic control had the option to use LU-PZE as an additional resource for learning. LU-PZE offers users randomized quizzes, structured assignments, and real-time visualizations of theoretical concepts. An analysis of student usage shows that LU-PZE successfully increased student engagement and improved their understanding of automatic control principles. Student feedback also reveals that the students appreciate interactive tools for learning. These findings suggest that LU-PZE can be a valuable tool for teachers to promote active learning and enhance student outcomes in introductory courses in automatic control.
{"title":"Student usage of Lund University Pole-Zero Explorer interactive tool in automatic control teaching","authors":"Frida Heskebeck , Pex Tufvesson , Ask Hällström","doi":"10.1016/j.ejcon.2025.101313","DOIUrl":"10.1016/j.ejcon.2025.101313","url":null,"abstract":"<div><div>This study examines the effectiveness of LU-PZE<span><span><sup>3</sup></span></span>, a web-based interactive tool designed to help students visualize and interact with fundamental concepts in automatic control. A total of 200 students enrolled in an introductory course on automatic control had the option to use LU-PZE as an additional resource for learning. LU-PZE offers users randomized quizzes, structured assignments, and real-time visualizations of theoretical concepts. An analysis of student usage shows that LU-PZE successfully increased student engagement and improved their understanding of automatic control principles. Student feedback also reveals that the students appreciate interactive tools for learning. These findings suggest that LU-PZE can be a valuable tool for teachers to promote active learning and enhance student outcomes in introductory courses in automatic control.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101313"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ejcon.2025.101362
Lampros N. Bikas, Anastasia-Kyriaki G. Mavridou, George A. Rovithakis
This paper addresses the enhancement of prescribed performance control (PPC) robustness under inelastic actuator faults. PPC is a control strategy that enforces user-defined bounds on output tracking errors to ensure desired transient and steady-state performance, assuming ideal actuator functioning. However, actuator faults compromise this guarantee, risking violation of performance bounds and potential system instability. To overcome this limitation, a robust modification of the conventional PPC is proposed, capable of effectively managing errors during performance bound violations while maintaining closed-loop stability. The approach ensures finite-time error recovery to the prescribed bounds once faults are mitigated, without relying on fault detection mechanisms, thereby simplifying the control scheme. Theoretical analysis confirms the stability and robustness of the proposed controller, and simulation studies validate its effectiveness.
{"title":"Fault-tolerant prescribed performance control for a class of uncertain nonlinear systems","authors":"Lampros N. Bikas, Anastasia-Kyriaki G. Mavridou, George A. Rovithakis","doi":"10.1016/j.ejcon.2025.101362","DOIUrl":"10.1016/j.ejcon.2025.101362","url":null,"abstract":"<div><div>This paper addresses the enhancement of prescribed performance control (PPC) robustness under inelastic actuator faults. PPC is a control strategy that enforces user-defined bounds on output tracking errors to ensure desired transient and steady-state performance, assuming ideal actuator functioning. However, actuator faults compromise this guarantee, risking violation of performance bounds and potential system instability. To overcome this limitation, a robust modification of the conventional PPC is proposed, capable of effectively managing errors during performance bound violations while maintaining closed-loop stability. The approach ensures finite-time error recovery to the prescribed bounds once faults are mitigated, without relying on fault detection mechanisms, thereby simplifying the control scheme. Theoretical analysis confirms the stability and robustness of the proposed controller, and simulation studies validate its effectiveness.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101362"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper deals with nonlinear boundary stabilization of a 1D reaction–diffusion equation with input delay. Using the modal decomposition approach, we propose a homogeneous-based predictor feedback for stabilizing the unstable modes. We prove the stability of the closed-loop system via the construction of a suitable Lyapunov functional. We present numerical simulations to support the analytical results and compare our proposed controller to linear predictor feedback regarding closed-loop performance and peaking effect.
{"title":"Homogeneous predictor feedback for a 1D reaction–diffusion equation with input delay","authors":"Mericel Ayamou , Nicolas Espitia , Andrey Polyakov , Emilia Fridman","doi":"10.1016/j.ejcon.2025.101363","DOIUrl":"10.1016/j.ejcon.2025.101363","url":null,"abstract":"<div><div>This paper deals with nonlinear boundary stabilization of a 1D reaction–diffusion equation with input delay. Using the modal decomposition approach, we propose a <em>homogeneous</em>-based predictor feedback for stabilizing the unstable modes. We prove the stability of the closed-loop system via the construction of a suitable Lyapunov functional. We present numerical simulations to support the analytical results and compare our proposed controller to linear predictor feedback regarding closed-loop performance and peaking effect.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101363"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ejcon.2025.101372
Tatiana Tatarenko , Maryam Kamgarpour
We consider generalized Nash equilibrium (GNE) problems in games with strongly monotone pseudo-gradients and jointly linear coupling constraints. We establish the convergence rate of a payoff-based approach intended to learn a variational GNE (v-GNE) in such games. While convergent algorithms have recently been proposed in this setting given full or partial information of the gradients, rate of convergence in the payoff-based information setting has been an open problem. Leveraging properties of a game extended from the original one by a dual player, we establish a convergence rate of to a v-GNE of the game.
{"title":"Convergence rate of payoff-based generalized Nash equilibrium learning","authors":"Tatiana Tatarenko , Maryam Kamgarpour","doi":"10.1016/j.ejcon.2025.101372","DOIUrl":"10.1016/j.ejcon.2025.101372","url":null,"abstract":"<div><div>We consider generalized Nash equilibrium (GNE) problems in games with strongly monotone pseudo-gradients and jointly linear coupling constraints. We establish the convergence rate of a payoff-based approach intended to learn a variational GNE (v-GNE) in such games. While convergent algorithms have recently been proposed in this setting given full or partial information of the gradients, rate of convergence in the payoff-based information setting has been an open problem. Leveraging properties of a game extended from the original one by a dual player, we establish a convergence rate of <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mfrac><mrow><mn>1</mn></mrow><mrow><msup><mrow><mi>t</mi></mrow><mrow><mn>4</mn><mo>/</mo><mn>7</mn></mrow></msup></mrow></mfrac><mo>)</mo></mrow></mrow></math></span> to a v-GNE of the game.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101372"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ejcon.2025.101373
Yifan Xie, Julian Berberich, Felix Brändle, Frank Allgöwer
This paper presents a data-driven min–max model predictive control (MPC) scheme for linear parameter-varying (LPV) systems. The goal is to steer the system to the origin while ensuring that the closed-loop system satisfies input and state constraints. Contrary to existing data-driven LPV control approaches, we assume that the scheduling signal is unknown during offline data collection and online system operation. Assuming a quadratic matrix inequality (QMI) description for the scheduling signal, we develop a novel data-driven characterization of the consistent system matrices using only input-state data. The proposed data-driven min–max MPC minimizes a tractable upper bound on the worst-case cost over the consistent system matrices set and all scheduling signals satisfying the QMI. The proposed approach guarantees recursive feasibility, closed-loop exponential stability and constraint satisfaction if it is feasible at the initial time. We demonstrate the effectiveness of the proposed method in simulation.
{"title":"Data-driven min–max MPC for LPV systems with unknown scheduling signal","authors":"Yifan Xie, Julian Berberich, Felix Brändle, Frank Allgöwer","doi":"10.1016/j.ejcon.2025.101373","DOIUrl":"10.1016/j.ejcon.2025.101373","url":null,"abstract":"<div><div>This paper presents a data-driven min–max model predictive control (MPC) scheme for linear parameter-varying (LPV) systems. The goal is to steer the system to the origin while ensuring that the closed-loop system satisfies input and state constraints. Contrary to existing data-driven LPV control approaches, we assume that the scheduling signal is unknown during offline data collection and online system operation. Assuming a quadratic matrix inequality (QMI) description for the scheduling signal, we develop a novel data-driven characterization of the consistent system matrices using only input-state data. The proposed data-driven min–max MPC minimizes a tractable upper bound on the worst-case cost over the consistent system matrices set and all scheduling signals satisfying the QMI. The proposed approach guarantees recursive feasibility, closed-loop exponential stability and constraint satisfaction if it is feasible at the initial time. We demonstrate the effectiveness of the proposed method in simulation.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101373"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ejcon.2025.101326
Jiayue Wang , Hamid Reza Feyzmahdavian, Soroush Rastegarpour, Alf J. Isaksson
This paper presents a novel dual-agent reinforcement learning (Rl) framework designed to improve the robustness of Rl-based control in industrial processes. To address the challenges posed by modeling errors and uncertainties, the framework draws inspiration from the robust control principles of tube Mpc. It features two specialized Rl agents: a nominal agent trained on an idealized process model in a simulated environment and a corrective agent trained to compensate for the discrepancies between the simulated model and actual system dynamics. To ensure realistic error compensation without direct access to the physical process, the simulation is systematically perturbed during training using parametric variations and Gaussian disturbances. A comprehensive case study demonstrates the effectiveness of the proposed method, highlighting its ability to maintain stable, reliable, and adaptive control performance under uncertain operating conditions in industrial applications.
{"title":"Robust tube-based reinforcement learning control for systems with parametric uncertainty","authors":"Jiayue Wang , Hamid Reza Feyzmahdavian, Soroush Rastegarpour, Alf J. Isaksson","doi":"10.1016/j.ejcon.2025.101326","DOIUrl":"10.1016/j.ejcon.2025.101326","url":null,"abstract":"<div><div>This paper presents a novel dual-agent reinforcement learning (<span>Rl</span>) framework designed to improve the robustness of <span>Rl</span>-based control in industrial processes. To address the challenges posed by modeling errors and uncertainties, the framework draws inspiration from the robust control principles of tube <span>Mpc</span>. It features two specialized <span>Rl</span> agents: a nominal agent trained on an idealized process model in a simulated environment and a corrective agent trained to compensate for the discrepancies between the simulated model and actual system dynamics. To ensure realistic error compensation without direct access to the physical process, the simulation is systematically perturbed during training using parametric variations and Gaussian disturbances. A comprehensive case study demonstrates the effectiveness of the proposed method, highlighting its ability to maintain stable, reliable, and adaptive control performance under uncertain operating conditions in industrial applications.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101326"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ejcon.2025.101287
Lorenzo Govoni, Andrea Cristofaro
This paper introduces the notion of input-to-task redundancy in multi-agent systems and extends the dynamic control allocation paradigm to input-constrained multi-agent systems. In particular, it is shown that a proper selection of a subset of the system outputs can entail a weak redundancy condition with respect to a given task to be executed, thereby providing additional degrees of freedom to cope with potential input constraints. Moreover, we propose a way of quantitatively characterizing the additional freedom in the controllability of the system by means of a redundancy degree suitably defined. The efficacy of the approach has been tested and validated by numerical simulations in a platooning application.
{"title":"Input-to-task redundancy and dynamic control allocation for multi-agent systems","authors":"Lorenzo Govoni, Andrea Cristofaro","doi":"10.1016/j.ejcon.2025.101287","DOIUrl":"10.1016/j.ejcon.2025.101287","url":null,"abstract":"<div><div>This paper introduces the notion of input-to-task redundancy in multi-agent systems and extends the dynamic control allocation paradigm to input-constrained multi-agent systems. In particular, it is shown that a proper selection of a subset of the system outputs can entail a weak redundancy condition with respect to a given task to be executed, thereby providing additional degrees of freedom to cope with potential input constraints. Moreover, we propose a way of quantitatively characterizing the additional freedom in the controllability of the system by means of a <em>redundancy degree</em> suitably defined. The efficacy of the approach has been tested and validated by numerical simulations in a platooning application.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101287"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ejcon.2025.101325
Anusree Rajan , Kushagra Parmeshwar , Pavankumar Tallapragada
This paper proposes an event-triggered parameterized control method using a control Lyapunov function approach for discrete time linear systems with external disturbances. In this control method, each control input to the plant is a linear combination of a fixed set of linearly independent scalar functions. The controller updates the coefficients of the parameterized control input in an event-triggered manner so as to minimize a quadratic cost function subject to quadratic constraints and communicates the same to the actuator. We design an event-triggering rule that guarantees global uniform ultimate boundedness of trajectories of the closed loop system and non-trivial inter-event times. We illustrate our results through a numerical example and we also compare the performance of the proposed control method with other existing control methods in the literature.
{"title":"A control Lyapunov function approach to event-triggered parameterized control for discrete-time linear systems","authors":"Anusree Rajan , Kushagra Parmeshwar , Pavankumar Tallapragada","doi":"10.1016/j.ejcon.2025.101325","DOIUrl":"10.1016/j.ejcon.2025.101325","url":null,"abstract":"<div><div>This paper proposes an event-triggered parameterized control method using a control Lyapunov function approach for discrete time linear systems with external disturbances. In this control method, each control input to the plant is a linear combination of a fixed set of linearly independent scalar functions. The controller updates the coefficients of the parameterized control input in an event-triggered manner so as to minimize a quadratic cost function subject to quadratic constraints and communicates the same to the actuator. We design an event-triggering rule that guarantees global uniform ultimate boundedness of trajectories of the closed loop system and non-trivial inter-event times. We illustrate our results through a numerical example and we also compare the performance of the proposed control method with other existing control methods in the literature.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101325"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ejcon.2025.101299
Michelangelo Faleschini , Damiano Rotondo , Maria Letizia Corradini
This paper presents the application of a recently proposed data-driven Linear Parameter Varying (LPV) controller design method to the Quanser Aero. The employed approach is based on Quadratic Matrix Inequalities (QMIs) and the strict matrix S-lemma. The existing approach is tweaked with the aim of forcing a higher control aggressiveness. Simulation and experimental results involving stabilization and multi-step-reference tracking are used to illustrate the validity and performance of the design.
{"title":"Data-driven LPV control based on Quadratic Matrix Inequalities: Experimental application to the Quanser Aero","authors":"Michelangelo Faleschini , Damiano Rotondo , Maria Letizia Corradini","doi":"10.1016/j.ejcon.2025.101299","DOIUrl":"10.1016/j.ejcon.2025.101299","url":null,"abstract":"<div><div>This paper presents the application of a recently proposed data-driven Linear Parameter Varying (LPV) controller design method to the Quanser Aero. The employed approach is based on Quadratic Matrix Inequalities (QMIs) and the strict matrix S-lemma. The existing approach is tweaked with the aim of forcing a higher control aggressiveness. Simulation and experimental results involving stabilization and multi-step-reference tracking are used to illustrate the validity and performance of the design.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101299"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ejcon.2025.101288
Ziling Li , Yiming Wan , Xueqian Wang , Feng Xu
This paper extends an asymptotic active fault diagnosis (AFD) framework using a bank of observers to linear time-invariant (LTI) systems under the effect of hybrid bounded and Gaussian uncertainties. Each observer is designed to match a healthy/faulty actuator mode. Bounded uncertainties are represented using zonotopes, while Gaussian uncertainties are modeled as ellipsoids based on the specified confidence level. The output confidence domains are expressed as the Minkowski sum of a zonotope and an ellipsoid, i.e., an ellipsotope. At each step, the proposed AFD method designs an input and a group of observer gains such that output confidence domains estimated by observers at the next step keep away from each other as far as possible. By designing inputs and injecting them into the system and optimizing observer gains step by step, AFD is eventually achieved. At the end of this paper, the effectiveness of the proposed method is illustrated by examples.
{"title":"Observer-based asymptotic active fault diagnosis under hybrid bounded and Gaussian uncertainties","authors":"Ziling Li , Yiming Wan , Xueqian Wang , Feng Xu","doi":"10.1016/j.ejcon.2025.101288","DOIUrl":"10.1016/j.ejcon.2025.101288","url":null,"abstract":"<div><div>This paper extends an asymptotic active fault diagnosis (AFD) framework using a bank of observers to linear time-invariant (LTI) systems under the effect of hybrid bounded and Gaussian uncertainties. Each observer is designed to match a healthy/faulty actuator mode. Bounded uncertainties are represented using zonotopes, while Gaussian uncertainties are modeled as ellipsoids based on the specified confidence level. The output confidence domains are expressed as the Minkowski sum of a zonotope and an ellipsoid, i.e., an ellipsotope. At each step, the proposed AFD method designs an input and a group of observer gains such that output confidence domains estimated by observers at the next step keep away from each other as far as possible. By designing inputs and injecting them into the system and optimizing observer gains step by step, AFD is eventually achieved. At the end of this paper, the effectiveness of the proposed method is illustrated by examples.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101288"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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