Pub Date : 2024-06-19DOI: 10.1109/LCSYS.2024.3417174
Jorn van Kampen;Mauro Moriggi;Francesco Braghin;Mauro Salazar
This letter presents model predictive control strategies for battery electric endurance race cars accounting for interactions with the competitors. In particular, we devise an optimization framework capturing the impact of the actions of the ego vehicle when interacting with competitors in a probabilistic fashion, jointly accounting for the optimal pit stop decision making, the charge times and the driving style in the course of the race. We showcase our method for a simulated 1 h endurance race at the Zandvoort circuit, using real-life data from a previous event. Our results show that optimizing both the race strategy and the decision making during the race is very important, resulting in a significant 21 s advantage over an always overtake approach, whilst revealing the competitiveness of e-race cars w.r.t. conventional ones.
{"title":"Model Predictive Control Strategies for Electric Endurance Race Cars Accounting for Competitors’ Interactions","authors":"Jorn van Kampen;Mauro Moriggi;Francesco Braghin;Mauro Salazar","doi":"10.1109/LCSYS.2024.3417174","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3417174","url":null,"abstract":"This letter presents model predictive control strategies for battery electric endurance race cars accounting for interactions with the competitors. In particular, we devise an optimization framework capturing the impact of the actions of the ego vehicle when interacting with competitors in a probabilistic fashion, jointly accounting for the optimal pit stop decision making, the charge times and the driving style in the course of the race. We showcase our method for a simulated 1 h endurance race at the Zandvoort circuit, using real-life data from a previous event. Our results show that optimizing both the race strategy and the decision making during the race is very important, resulting in a significant 21 s advantage over an always overtake approach, whilst revealing the competitiveness of e-race cars w.r.t. conventional ones.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602561","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 : 2024-06-19DOI: 10.1109/LCSYS.2024.3417173
Keitaro Yamamoto;Kenji Fujimoto;Ichiro Maruta
This letter proposes an algorithm for solving finite-time nonlinear optimal control problems. The proposed method employs the Gauss pseudospectral method to transform the optimal control problem into a nonlinear programming problem, and sequential convex programming (SCP) to solve it. Furthermore, by applying the information of the solution obtained by SCP to the indirect shooting method, a more accurate optimal solution can be obtained. There was an attempt to solve a similar class of optimal control problems, but it was only applicable to a restrictive class of problems without state constraints. In contrast, the proposed method can solve a general class of optimal control problems, including those with state constraints, while ensuring the numerical stability of the algorithm. This objective is achieved without losing the numerical stability of the algorithm by introducing a slack variable and incorporating state constraints into the dynamics. Additionally, the proposed method guarantees quadratic convergence by appropriately limiting the update step size of the optimization variables. To demonstrate the effectiveness of the proposed method, we apply the proposed method to an $L^{1}/L^{2}$ -optimal control problem of a two-wheeled rover.
{"title":"Guaranteed Pseudospectral Sequential Convex Programming for Accurate Solutions to Constrained Optimal Control Problems","authors":"Keitaro Yamamoto;Kenji Fujimoto;Ichiro Maruta","doi":"10.1109/LCSYS.2024.3417173","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3417173","url":null,"abstract":"This letter proposes an algorithm for solving finite-time nonlinear optimal control problems. The proposed method employs the Gauss pseudospectral method to transform the optimal control problem into a nonlinear programming problem, and sequential convex programming (SCP) to solve it. Furthermore, by applying the information of the solution obtained by SCP to the indirect shooting method, a more accurate optimal solution can be obtained. There was an attempt to solve a similar class of optimal control problems, but it was only applicable to a restrictive class of problems without state constraints. In contrast, the proposed method can solve a general class of optimal control problems, including those with state constraints, while ensuring the numerical stability of the algorithm. This objective is achieved without losing the numerical stability of the algorithm by introducing a slack variable and incorporating state constraints into the dynamics. Additionally, the proposed method guarantees quadratic convergence by appropriately limiting the update step size of the optimization variables. To demonstrate the effectiveness of the proposed method, we apply the proposed method to an \u0000<inline-formula> <tex-math>$L^{1}/L^{2}$ </tex-math></inline-formula>\u0000-optimal control problem of a two-wheeled rover.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141583557","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 : 2024-06-19DOI: 10.1109/LCSYS.2024.3416858
Mohammad Afshari;Dipankar Maity;Panagiotis Tsiotras
We consider a multi-agent linear quadratic optimal control problem. Due to communication constraints, the agents are required to quantize their local state measurements before communicating them to the rest of the team, thus resulting in a decentralized information structure. The optimal controllers are to be synthesized under this decentralized and quantized information structure. The agents are given a set of quantizers with varying quantization resolutions—higher resolution incurs higher communication cost and vice versa. The team must optimally select the quantizer to prioritize agents with ‘high-quality’ information for optimizing the control performance under communication constraints. We show that there exist a separation between the optimal solution to the control problem and the choice of the optimal quantizer. We show that the optimal controllers are linear and the optimal selection of the quantizers can be determined by solving a linear program.
{"title":"Communication- and Control-Aware Optimal Quantizer Selection for Multi-Agent Control","authors":"Mohammad Afshari;Dipankar Maity;Panagiotis Tsiotras","doi":"10.1109/LCSYS.2024.3416858","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3416858","url":null,"abstract":"We consider a multi-agent linear quadratic optimal control problem. Due to communication constraints, the agents are required to quantize their local state measurements before communicating them to the rest of the team, thus resulting in a decentralized information structure. The optimal controllers are to be synthesized under this decentralized and quantized information structure. The agents are given a set of quantizers with varying quantization resolutions—higher resolution incurs higher communication cost and vice versa. The team must optimally select the quantizer to prioritize agents with ‘high-quality’ information for optimizing the control performance under communication constraints. We show that there exist a separation between the optimal solution to the control problem and the choice of the optimal quantizer. We show that the optimal controllers are linear and the optimal selection of the quantizers can be determined by solving a linear program.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555121","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 : 2024-06-19DOI: 10.1109/LCSYS.2024.3417171
Diego Deplano;Mauro Franceschelli;Alessandro Giua
This letter shows that trajectories of continuous-time monotone systems (in the sense of Kamke-Muller) converge to equilibrium points if their vector field is continuously differentiable and if they are nonexpansive w.r.t. a diagonally weighted infinity norm. Differently from the current literature trend, the system is not required to be contractive but merely nonexpansive, thus allowing for multiple equilibrium points. Easy-to-check conditions on the vector field to verify that the system is both monotone and nonexpansive are provided. This is done by showing that nonexpansiveness is implied by subhomogeneity of the system, a generalization of the translation invariance property. We apply the results in the context of RNNs, thus providing sufficient conditions for convergence of the state trajectories of nonexpansive monotone neural networks that are not contractive.
{"title":"Stability of Nonexpansive Monotone Systems and Application to Recurrent Neural Networks","authors":"Diego Deplano;Mauro Franceschelli;Alessandro Giua","doi":"10.1109/LCSYS.2024.3417171","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3417171","url":null,"abstract":"This letter shows that trajectories of continuous-time monotone systems (in the sense of Kamke-Muller) converge to equilibrium points if their vector field is continuously differentiable and if they are nonexpansive w.r.t. a diagonally weighted infinity norm. Differently from the current literature trend, the system is not required to be contractive but merely nonexpansive, thus allowing for multiple equilibrium points. Easy-to-check conditions on the vector field to verify that the system is both monotone and nonexpansive are provided. This is done by showing that nonexpansiveness is implied by subhomogeneity of the system, a generalization of the translation invariance property. We apply the results in the context of RNNs, thus providing sufficient conditions for convergence of the state trajectories of nonexpansive monotone neural networks that are not contractive.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10565791","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602508","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}
We study a class of stochastic Generalized Linear Switched System (GLSS), which includes subclasses of jump-Markov, piecewise-linear and Linear Parameter-Varying (LPV) systems. We prove that the output of such systems can be decomposed into deterministic and stochastic components. Using this decomposition, we show existence of state-space representation in innovation form, and we provide sufficient conditions for such representations to be minimal and unique up to isomorphism.
{"title":"Toward Stochastic Realization Theory for Generalized Linear Switched Systems With Inputs: Decomposition Into Stochastic and Deterministic Components and Existence and Uniqueness of Innovation Form","authors":"Elie Rouphael;Manas Mejari;Mihaly Petreczky;Lotfi Belkoura","doi":"10.1109/LCSYS.2024.3417189","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3417189","url":null,"abstract":"We study a class of stochastic Generalized Linear Switched System (GLSS), which includes subclasses of jump-Markov, piecewise-linear and Linear Parameter-Varying (LPV) systems. We prove that the output of such systems can be decomposed into deterministic and stochastic components. Using this decomposition, we show existence of state-space representation in innovation form, and we provide sufficient conditions for such representations to be minimal and unique up to isomorphism.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602488","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}
Researchers develop models to explain the unknowns. These models typically involve parameters that capture tangible quantities, the estimation of which is desired. Parameter identifiability investigates the recoverability of the unknown parameters given the error-free outputs, inputs, and the developed equations of the model. Different notions of and methods to test identifiability exist for dynamical systems defined in the continuous space. Yet little attention was paid to the identifiability of discrete space systems, where variables and parameters are defined in a discrete space. We develop the identifiability framework for discrete space systems and highlight that this is not an immediate extension of the continuous space framework. Unlike the continuous case, local identifiability concepts are sensitive to how a “neighborhood” is defined. Moreover, results on algebraic identifiability that proved useful in the continuous space are less so in their discrete form as the notion of differentiability disappears.
{"title":"Extending Identifiability Results From Continuous to Discrete-Space Systems","authors":"Anuththara Sarathchandra;Azadeh Aghaeeyan;Pouria Ramazi","doi":"10.1109/LCSYS.2024.3416238","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3416238","url":null,"abstract":"Researchers develop models to explain the unknowns. These models typically involve parameters that capture tangible quantities, the estimation of which is desired. Parameter identifiability investigates the recoverability of the unknown parameters given the error-free outputs, inputs, and the developed equations of the model. Different notions of and methods to test identifiability exist for dynamical systems defined in the continuous space. Yet little attention was paid to the identifiability of discrete space systems, where variables and parameters are defined in a discrete space. We develop the identifiability framework for discrete space systems and highlight that this is not an immediate extension of the continuous space framework. Unlike the continuous case, local identifiability concepts are sensitive to how a “neighborhood” is defined. Moreover, results on algebraic identifiability that proved useful in the continuous space are less so in their discrete form as the notion of differentiability disappears.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543993","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 : 2024-06-18DOI: 10.1109/LCSYS.2024.3416407
Ruikun Zhou;Maxwell Fitzsimmons;Yiming Meng;Jun Liu
We demonstrate that a convex optimization formulation of physics-informed neural networks for solving partial differential equations can address a variety of computationally challenging tasks in nonlinear system analysis and control. This includes computing Lyapunov functions, region-of-attraction estimates, and optimal controllers. Through numerical examples, we illustrate that the formulation is effective in solving both low- and high-dimensional analysis and control problems. We compare it with alternative approaches, including semidefinite programming and nonconvex neural network optimization, to demonstrate its potential advantages.
{"title":"Physics-Informed Extreme Learning Machine Lyapunov Functions","authors":"Ruikun Zhou;Maxwell Fitzsimmons;Yiming Meng;Jun Liu","doi":"10.1109/LCSYS.2024.3416407","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3416407","url":null,"abstract":"We demonstrate that a convex optimization formulation of physics-informed neural networks for solving partial differential equations can address a variety of computationally challenging tasks in nonlinear system analysis and control. This includes computing Lyapunov functions, region-of-attraction estimates, and optimal controllers. Through numerical examples, we illustrate that the formulation is effective in solving both low- and high-dimensional analysis and control problems. We compare it with alternative approaches, including semidefinite programming and nonconvex neural network optimization, to demonstrate its potential advantages.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602558","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 : 2024-06-18DOI: 10.1109/LCSYS.2024.3416337
Kushal Chakrabarti;Nikhil Chopra
This letter considers the observer design problem for discrete-time nonlinear dynamical systems with sampled measurements. The recently proposed Iteratively Preconditioned Gradient-Descent (IPG) observer, a Newton-type observer, has been empirically shown to have improved robustness against measurement noise than the prominent nonlinear observers, a property that other Newton-type observers lack. However, no theoretical guarantees on the convergence of the IPG observer were provided. This letter presents a rigorous convergence analysis of the IPG observer for a class of nonlinear systems in deterministic settings, proving its local linear convergence to the actual trajectory. The assumptions are standard in the existing literature of Newton-type observers, and the analysis further confirms the relation of IPG observer with Newton observer, which was only hypothesized earlier.
{"title":"On Convergence of the Iteratively Preconditioned Gradient-Descent (IPG) Observer","authors":"Kushal Chakrabarti;Nikhil Chopra","doi":"10.1109/LCSYS.2024.3416337","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3416337","url":null,"abstract":"This letter considers the observer design problem for discrete-time nonlinear dynamical systems with sampled measurements. The recently proposed Iteratively Preconditioned Gradient-Descent (IPG) observer, a Newton-type observer, has been empirically shown to have improved robustness against measurement noise than the prominent nonlinear observers, a property that other Newton-type observers lack. However, no theoretical guarantees on the convergence of the IPG observer were provided. This letter presents a rigorous convergence analysis of the IPG observer for a class of nonlinear systems in deterministic settings, proving its local linear convergence to the actual trajectory. The assumptions are standard in the existing literature of Newton-type observers, and the analysis further confirms the relation of IPG observer with Newton observer, which was only hypothesized earlier.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141544004","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 : 2024-06-18DOI: 10.1109/LCSYS.2024.3416239
Ersin Daş;Aaron D. Ames;Joel W. Burdick
This letter develops rollover prevention guarantees for mobile robots using control barrier function (CBF) theory, and demonstrates the method experimentally. We consider a safety measure based on a zero moment point condition through the lens of CBFs. However, these conditions depend on time-varying and noisy parameters. To address this issue, we present a differentiator-based safety-critical controller that estimates these parameters and pairs Input-to-State Stable (ISS) differentiator dynamics with CBFs to achieve rigorous safety guarantees. Additionally, to ensure safety in the presence of disturbances, we utilize a time-varying extension of Projection-to-State Safety (PSSf). The effectiveness of the proposed method is demonstrated via experiments on a tracked robot with a rollover potential on steep slopes.
{"title":"Rollover Prevention for Mobile Robots With Control Barrier Functions: Differentiator-Based Adaptation and Projection-to-State Safety","authors":"Ersin Daş;Aaron D. Ames;Joel W. Burdick","doi":"10.1109/LCSYS.2024.3416239","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3416239","url":null,"abstract":"This letter develops rollover prevention guarantees for mobile robots using control barrier function (CBF) theory, and demonstrates the method experimentally. We consider a safety measure based on a zero moment point condition through the lens of CBFs. However, these conditions depend on time-varying and noisy parameters. To address this issue, we present a differentiator-based safety-critical controller that estimates these parameters and pairs Input-to-State Stable (ISS) differentiator dynamics with CBFs to achieve rigorous safety guarantees. Additionally, to ensure safety in the presence of disturbances, we utilize a time-varying extension of Projection-to-State Safety (PSSf). The effectiveness of the proposed method is demonstrated via experiments on a tracked robot with a rollover potential on steep slopes.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602496","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 is dedicated to symmetric and asymmetric fault detection, isolation, and estimation of a three-phase DC-AC (Direct Current-Alternating Current) inverter connected to the grid by three LCL filters. The system modeling and the faults estimation algorithm are performed in the abc frame allowing to take into account phase by phase asymmetrical faults. It is based on a left inversion technique and uses the super-twisting differentiator in order to estimate the state components of the system from the given measurements. Simulations to support the proposed approach are presented.
{"title":"Fault Detection, Isolation, and Estimation for a Three-Phase Grid Connected DC-AC Inverter With LCL Filters","authors":"Marouane Laaziz;Florentina Nicolau;Malek Ghanes;Jean-Pierre Barbot;Robert Boisliveau;Nadia Machkour","doi":"10.1109/LCSYS.2024.3416404","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3416404","url":null,"abstract":"This letter is dedicated to symmetric and asymmetric fault detection, isolation, and estimation of a three-phase DC-AC (Direct Current-Alternating Current) inverter connected to the grid by three LCL filters. The system modeling and the faults estimation algorithm are performed in the abc frame allowing to take into account phase by phase asymmetrical faults. It is based on a left inversion technique and uses the super-twisting differentiator in order to estimate the state components of the system from the given measurements. Simulations to support the proposed approach are presented.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602560","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}
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