Pub Date : 2024-12-19DOI: 10.1109/LCSYS.2024.3520918
J. Escareno;J. U. Alvarez-Munoz;L. R. Garcia Carrillo;I. Rubio Scola;J. Franco-Robles;O. Labbani-Igbida
Nonlinearity and uncertainty are major features in control systems. In this context, the present work proposes to merge the brain emotional learning model with the benefits of robust event-driven control to handle uncertain nonlinear systems. The state-dependent unmodeled dynamics is estimated via the limbic system-inspired learning algorithm and added to the nominal control signal for compensation purposes. Furthermore, aiming at reducing data processing, and inherently, computational cost, the controller is triggered asynchronously driven by events function. Moreover, the closed-loop stability of the proposed control scheme is verified through the Lyapunov formalism, as well as the sampling admissibility to prevent the Zeno phenomena. The performance observed in the numerical results witnesses the effectiveness of the proposed control scheme.
{"title":"Limbic System-Inspired Robust Event-Driven Control for High-Order Uncertain Nonlinear Systems","authors":"J. Escareno;J. U. Alvarez-Munoz;L. R. Garcia Carrillo;I. Rubio Scola;J. Franco-Robles;O. Labbani-Igbida","doi":"10.1109/LCSYS.2024.3520918","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3520918","url":null,"abstract":"Nonlinearity and uncertainty are major features in control systems. In this context, the present work proposes to merge the brain emotional learning model with the benefits of robust event-driven control to handle uncertain nonlinear systems. The state-dependent unmodeled dynamics is estimated via the limbic system-inspired learning algorithm and added to the nominal control signal for compensation purposes. Furthermore, aiming at reducing data processing, and inherently, computational cost, the controller is triggered asynchronously driven by events function. Moreover, the closed-loop stability of the proposed control scheme is verified through the Lyapunov formalism, as well as the sampling admissibility to prevent the Zeno phenomena. The performance observed in the numerical results witnesses the effectiveness of the proposed control scheme.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3057-3062"},"PeriodicalIF":2.4,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-19DOI: 10.1109/LCSYS.2024.3520348
Melanie Gallant;Christoph Mark;Paolo Pazzaglia;Johannes von Keler;Laura Beermann;Kevin Schmidt;Martina Maggio
The contribution of this letter is the mean-square stabilization of discrete-time Markov jump linear systems with mixed known, unknown, and time-varying transition probabilities. To handle uncertainties in the transition probabilities, we develop a control strategy utilizing mode-dependent static state feedback controllers and introduce data-based ambiguity sets that, extending existing literature, account for known, unknown and time-varying probabilities. These ambiguity sets are constructed using estimated transition matrices and probabilistic bounds derived from the Dvoretzky-Kiefer-Wolfowitz inequality. We validate the effectiveness of our method with numerical simulations on a control system subject to deadline overruns, demonstrating the improvements of incorporating partial knowledge of the transition probabilities.
{"title":"Structure-Exploiting Distributionally Robust Control of Non-Homogeneous Markov Jump Linear Systems","authors":"Melanie Gallant;Christoph Mark;Paolo Pazzaglia;Johannes von Keler;Laura Beermann;Kevin Schmidt;Martina Maggio","doi":"10.1109/LCSYS.2024.3520348","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3520348","url":null,"abstract":"The contribution of this letter is the mean-square stabilization of discrete-time Markov jump linear systems with mixed known, unknown, and time-varying transition probabilities. To handle uncertainties in the transition probabilities, we develop a control strategy utilizing mode-dependent static state feedback controllers and introduce data-based ambiguity sets that, extending existing literature, account for known, unknown and time-varying probabilities. These ambiguity sets are constructed using estimated transition matrices and probabilistic bounds derived from the Dvoretzky-Kiefer-Wolfowitz inequality. We validate the effectiveness of our method with numerical simulations on a control system subject to deadline overruns, demonstrating the improvements of incorporating partial knowledge of the transition probabilities.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3069-3074"},"PeriodicalIF":2.4,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-19DOI: 10.1109/LCSYS.2024.3520415
Meridian Haas;Joseph Drallmeier;Robert Middleton;Jason B. Siegel;Shima Nazari
Hybrid electric vehicles (HEV) enable reduction of emissions without sacrificing consumer expected range and drivability. The diversification of the powertrain with multiple power sources allows downsizing the internal combustion engine and implementing optimal energy management strategies. The interaction among components of an HEV are key to the overall efficiency. Therefore, efficiency potential is lost if this interdependence is neglected during the powertrain design by focusing on individual optimization of component specifications. This letter formulates and solves a co-design problem by integrating the energy management with the optimal powertrain and drivetrain component sizing for a hybrid powertrain equipped with an opposed piston (OP) engine in a series architecture. Our novel approach develops a model for an OP engine and integrates battery capacity degradation into the co-design problem. The optimal solution allows for a minimally sized engine that accounts for the average power requirements, and a large enough battery to provide fast power dynamics.
{"title":"Combined Design and Control Optimization for a Series Hybrid Electric Vehicle With an Opposed Piston Engine","authors":"Meridian Haas;Joseph Drallmeier;Robert Middleton;Jason B. Siegel;Shima Nazari","doi":"10.1109/LCSYS.2024.3520415","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3520415","url":null,"abstract":"Hybrid electric vehicles (HEV) enable reduction of emissions without sacrificing consumer expected range and drivability. The diversification of the powertrain with multiple power sources allows downsizing the internal combustion engine and implementing optimal energy management strategies. The interaction among components of an HEV are key to the overall efficiency. Therefore, efficiency potential is lost if this interdependence is neglected during the powertrain design by focusing on individual optimization of component specifications. This letter formulates and solves a co-design problem by integrating the energy management with the optimal powertrain and drivetrain component sizing for a hybrid powertrain equipped with an opposed piston (OP) engine in a series architecture. Our novel approach develops a model for an OP engine and integrates battery capacity degradation into the co-design problem. The optimal solution allows for a minimally sized engine that accounts for the average power requirements, and a large enough battery to provide fast power dynamics.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2973-2978"},"PeriodicalIF":2.4,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This letter develops the machinery of Koopman-based Model Predictive Control (KMPC) design, where the Koopman derived model is unable to capture the real nonlinear system perfectly. We then propose to use an MPC-based reinforcement learning within the Koopman framework combining the strengths of MPC, Reinforcement Learning (RL), and the Koopman Operator (KO) theory for an efficient data-driven control and performance-oriented learning of complex nonlinear systems. We show that the closed-loop performance of the KMPC is improved by modifying the KMPC objective function. In practice, we design a fully parameterized KMPC and employ RL to adjust the corresponding parameters aiming at achieving the best achievable closed-loop performance.
{"title":"Performance-Oriented Data-Driven Control: Fusing Koopman Operator and MPC-Based Reinforcement Learning","authors":"Hossein Nejatbakhsh Esfahani;Umesh Vaidya;Javad Mohammadpour Velni","doi":"10.1109/LCSYS.2024.3520904","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3520904","url":null,"abstract":"This letter develops the machinery of Koopman-based Model Predictive Control (KMPC) design, where the Koopman derived model is unable to capture the real nonlinear system perfectly. We then propose to use an MPC-based reinforcement learning within the Koopman framework combining the strengths of MPC, Reinforcement Learning (RL), and the Koopman Operator (KO) theory for an efficient data-driven control and performance-oriented learning of complex nonlinear systems. We show that the closed-loop performance of the KMPC is improved by modifying the KMPC objective function. In practice, we design a fully parameterized KMPC and employ RL to adjust the corresponding parameters aiming at achieving the best achievable closed-loop performance.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3021-3026"},"PeriodicalIF":2.4,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this letter, motion planning for a Dubins vehicle on a unit sphere to attain a desired final location is considered. The radius of the Dubins path on the sphere is lower bounded by r, where r represents the radius of the tightest left or right turn the vehicle can take on the sphere. Noting that $r in $ (0, 1) and can affect the trajectory taken by the vehicle, it is desired to determine the candidate optimal paths for r ranging from nearly zero to close to one to attain a desired final location. In a previous study, this problem was addressed, wherein it was shown that the optimal path is of type $CG, CC$ , or a degenerate path of the CG and CC paths, which includes C, G paths, for $r leq {}frac {1}{2}$ . Here, $C~in $ {$L, R$ } denotes an arc of a tight left or right turn of minimum turning radius r, and G denotes an arc of a great circle. In this letter, the candidate paths for the same problem are generalized to model vehicles with a larger turning radius. In particular, it is shown that the candidate optimal paths are of type $CG, CC$ , or a degenerate path of the CG and CC paths for $r leq {}frac {sqrt {3}}{2}$ . Noting that at most two LG paths and two RG paths can exist for a given final location, this letter further reduces the candidate optimal paths by showing that only one LG and one RG path can be optimal, yielding a total of seven candidate paths for $r leq {}frac {sqrt {3}}{2}$ . Additional conditions for the optimality of CC paths are also derived in this letter.
{"title":"Generalization of Optimal Geodesic Curvature Constrained Dubins’ Path on Sphere With Free Terminal Orientation","authors":"Deepak Prakash Kumar;Swaroop Darbha;Satyanarayana Gupta Manyam;David Casbeer","doi":"10.1109/LCSYS.2024.3520026","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3520026","url":null,"abstract":"In this letter, motion planning for a Dubins vehicle on a unit sphere to attain a desired final location is considered. The radius of the Dubins path on the sphere is lower bounded by r, where r represents the radius of the tightest left or right turn the vehicle can take on the sphere. Noting that <inline-formula> <tex-math>$r in $ </tex-math></inline-formula> (0, 1) and can affect the trajectory taken by the vehicle, it is desired to determine the candidate optimal paths for r ranging from nearly zero to close to one to attain a desired final location. In a previous study, this problem was addressed, wherein it was shown that the optimal path is of type <inline-formula> <tex-math>$CG, CC$ </tex-math></inline-formula>, or a degenerate path of the CG and CC paths, which includes C, G paths, for <inline-formula> <tex-math>$r leq {}frac {1}{2}$ </tex-math></inline-formula>. Here, <inline-formula> <tex-math>$C~in $ </tex-math></inline-formula> {<inline-formula> <tex-math>$L, R$ </tex-math></inline-formula>} denotes an arc of a tight left or right turn of minimum turning radius r, and G denotes an arc of a great circle. In this letter, the candidate paths for the same problem are generalized to model vehicles with a larger turning radius. In particular, it is shown that the candidate optimal paths are of type <inline-formula> <tex-math>$CG, CC$ </tex-math></inline-formula>, or a degenerate path of the CG and CC paths for <inline-formula> <tex-math>$r leq {}frac {sqrt {3}}{2}$ </tex-math></inline-formula>. Noting that at most two LG paths and two RG paths can exist for a given final location, this letter further reduces the candidate optimal paths by showing that only one LG and one RG path can be optimal, yielding a total of seven candidate paths for <inline-formula> <tex-math>$r leq {}frac {sqrt {3}}{2}$ </tex-math></inline-formula>. Additional conditions for the optimality of CC paths are also derived in this letter.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2991-2996"},"PeriodicalIF":2.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-18DOI: 10.1109/LCSYS.2024.3520060
Yichen Liu;Mohamad Amin Sharifi Kolarijani
In this letter, we consider the application of max-plus-linear approximators for Q-function in offline reinforcement learning of discounted Markov decision processes. In particular, we incorporate these approximators to propose novel fitted Q-iteration (FQI) algorithms with provable convergence. Exploiting the compatibility of the Bellman operator with max-plus operations, we show that the max-plus-linear regression within each iteration of the proposed FQI algorithm reduces to simple max-plus matrix-vector multiplications. We also consider the variational implementation of the proposed algorithm which leads to a per-iteration complexity that is independent of the number of samples.
{"title":"Fitted Q-Iteration via Max-Plus-Linear Approximation","authors":"Yichen Liu;Mohamad Amin Sharifi Kolarijani","doi":"10.1109/LCSYS.2024.3520060","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3520060","url":null,"abstract":"In this letter, we consider the application of max-plus-linear approximators for Q-function in offline reinforcement learning of discounted Markov decision processes. In particular, we incorporate these approximators to propose novel fitted Q-iteration (FQI) algorithms with provable convergence. Exploiting the compatibility of the Bellman operator with max-plus operations, we show that the max-plus-linear regression within each iteration of the proposed FQI algorithm reduces to simple max-plus matrix-vector multiplications. We also consider the variational implementation of the proposed algorithm which leads to a per-iteration complexity that is independent of the number of samples.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3201-3206"},"PeriodicalIF":2.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-18DOI: 10.1109/LCSYS.2024.3519547
Dorijan Leko;Mario Vašak
This letter presents an enhanced Trust Region Method (TRM) for Sequential Linear Programming (SLP) designed to improve the initial feasible solution to a constrained nonlinear programming problem while maintaining the interim solutions feasibility throughout the SLP iterations. The method employs a polytopic sub-approximation of the feasible region, defined around the interim solution as a level set based on variable limits for the linearization error. This polytopic feasible region is established by using a trust region that ensures that maximum limits of the linearization errors are respected. The method adaptively adjusts the size of the feasible region during iterations to achieve convergence to a local optimum by employing variable linearization error limits. Local convergence is attained by reducing the size of the trust radius. A case study illustrates the effectiveness of the proposed method, which is compared to the benchmark TRM that uses heuristic limits on the permissible changes in manipulated variables.
{"title":"Sequential Linear Programming With Adaptive Linearization Error Limits for All-Time Feasibility","authors":"Dorijan Leko;Mario Vašak","doi":"10.1109/LCSYS.2024.3519547","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3519547","url":null,"abstract":"This letter presents an enhanced Trust Region Method (TRM) for Sequential Linear Programming (SLP) designed to improve the initial feasible solution to a constrained nonlinear programming problem while maintaining the interim solutions feasibility throughout the SLP iterations. The method employs a polytopic sub-approximation of the feasible region, defined around the interim solution as a level set based on variable limits for the linearization error. This polytopic feasible region is established by using a trust region that ensures that maximum limits of the linearization errors are respected. The method adaptively adjusts the size of the feasible region during iterations to achieve convergence to a local optimum by employing variable linearization error limits. Local convergence is attained by reducing the size of the trust radius. A case study illustrates the effectiveness of the proposed method, which is compared to the benchmark TRM that uses heuristic limits on the permissible changes in manipulated variables.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3051-3056"},"PeriodicalIF":2.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10806887","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-18DOI: 10.1109/LCSYS.2024.3520025
Ali Kashani;Amy K. Strong;Leila J. Bridgeman;Claus Danielson
We present a novel direct data-driven method for computing constraint-admissible positive invariant sets for general nonlinear systems with compact constraint sets. Our approach employs machine learning techniques to lift the state space and approximate invariant sets using finite data. The invariant sets are parameterized as sub-level-sets of scalar linear functions in the lifted space, which is suitable for control applications. We provide probabilistic guarantees of invariance through scenario optimization, with probability bounds on robustness against the uncertainty inherent in the data-driven framework. As the amount of data increases, these probability bounds approach 1. We use our invariant sets to switch between a collection of controllers to select a controller which enforces constraints. We demonstrate the practicality of our method by applying it to a nonlinear autonomous driving lane-keeping scenario.
{"title":"Probabilistic Data-Driven Invariance for Constrained Control of Nonlinear Systems","authors":"Ali Kashani;Amy K. Strong;Leila J. Bridgeman;Claus Danielson","doi":"10.1109/LCSYS.2024.3520025","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3520025","url":null,"abstract":"We present a novel direct data-driven method for computing constraint-admissible positive invariant sets for general nonlinear systems with compact constraint sets. Our approach employs machine learning techniques to lift the state space and approximate invariant sets using finite data. The invariant sets are parameterized as sub-level-sets of scalar linear functions in the lifted space, which is suitable for control applications. We provide probabilistic guarantees of invariance through scenario optimization, with probability bounds on robustness against the uncertainty inherent in the data-driven framework. As the amount of data increases, these probability bounds approach 1. We use our invariant sets to switch between a collection of controllers to select a controller which enforces constraints. We demonstrate the practicality of our method by applying it to a nonlinear autonomous driving lane-keeping scenario.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3165-3170"},"PeriodicalIF":2.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-18DOI: 10.1109/LCSYS.2024.3520023
Dženan Lapandić;Fengze Xie;Christos K. Verginis;Soon-Jo Chung;Dimos V. Dimarogonas;Bo Wahlberg
A major challenge in autonomous flights is unknown disturbances, which can jeopardize safety and cause collisions, especially in obstacle-rich environments. This letter presents a disturbance-aware motion planning and control framework for autonomous aerial flights. The framework is composed of two key components: a disturbance-aware motion planner and a tracking controller. The motion planner consists of a predictive control scheme and an online-adapted learned disturbance model. The tracking controller, developed using contraction control methods, ensures safety bounds on the quadrotor’s behavior near obstacles with respect to the motion plan. The algorithm is tested in simulations with a quadrotor facing strong crosswind and ground-induced disturbances.
{"title":"Meta-Learning Augmented MPC for Disturbance-Aware Motion Planning and Control of Quadrotors","authors":"Dženan Lapandić;Fengze Xie;Christos K. Verginis;Soon-Jo Chung;Dimos V. Dimarogonas;Bo Wahlberg","doi":"10.1109/LCSYS.2024.3520023","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3520023","url":null,"abstract":"A major challenge in autonomous flights is unknown disturbances, which can jeopardize safety and cause collisions, especially in obstacle-rich environments. This letter presents a disturbance-aware motion planning and control framework for autonomous aerial flights. The framework is composed of two key components: a disturbance-aware motion planner and a tracking controller. The motion planner consists of a predictive control scheme and an online-adapted learned disturbance model. The tracking controller, developed using contraction control methods, ensures safety bounds on the quadrotor’s behavior near obstacles with respect to the motion plan. The algorithm is tested in simulations with a quadrotor facing strong crosswind and ground-induced disturbances.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3045-3050"},"PeriodicalIF":2.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-18DOI: 10.1109/LCSYS.2024.3520031
Tatsuya Miyano;Ryotaro Shima;Yuji Ito
In this letter, we present an adaptive passification framework for unknown input-affine nonlinear systems. In the present framework, a reference system is designed so that the deviation between the reference system and an unknown nominal system is minimized, while ensuring some classes of passivity properties. Based on the passive reference system, we present an adaptive control method that drives the nominal system to the reference system. The performance of the present framework was demonstrated through numerical experiments.
{"title":"Adaptive Passification of Unknown Input-Affine Nonlinear Systems","authors":"Tatsuya Miyano;Ryotaro Shima;Yuji Ito","doi":"10.1109/LCSYS.2024.3520031","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3520031","url":null,"abstract":"In this letter, we present an adaptive passification framework for unknown input-affine nonlinear systems. In the present framework, a reference system is designed so that the deviation between the reference system and an unknown nominal system is minimized, while ensuring some classes of passivity properties. Based on the passive reference system, we present an adaptive control method that drives the nominal system to the reference system. The performance of the present framework was demonstrated through numerical experiments.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2979-2984"},"PeriodicalIF":2.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10806861","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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