Pub Date : 2024-12-09DOI: 10.1109/LCSYS.2024.3514977
Rhianna M. Oakley;Andrew T. Polonsky;Paul Chao;Claus Danielson
This letter presents a one-step predictive run-to-run controller (R2R-MPC) for the automation of mechanical serial sectioning (MSS), a destructive material analysis process. To address the inherent uncertainty and disturbances in the MSS process, a robust closed-loop approach is presented. The robust R2R-MPC models the uncertainty of the MSS process using a linear differential inclusion. As an analytical model of the MSS process is unavailable, the differential inclusion is identified from historical data. The R2R-MPC is posed as an optimization problem that computes incremental changes to the control input which minimize the worst-case material removal errors. This optimization-based controller is combined with a run-to-run controller to provide integral action that rejects constant disturbances and tracks constant reference removal rates. To demonstrate the efficacy of our robust R2R-MPC, we present simulation results which compare the presented controller with a conventional non-robust R2R.
{"title":"Robust Data-Driven Predictive Run-to-Run Control for Automated Serial Sectioning","authors":"Rhianna M. Oakley;Andrew T. Polonsky;Paul Chao;Claus Danielson","doi":"10.1109/LCSYS.2024.3514977","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3514977","url":null,"abstract":"This letter presents a one-step predictive run-to-run controller (R2R-MPC) for the automation of mechanical serial sectioning (MSS), a destructive material analysis process. To address the inherent uncertainty and disturbances in the MSS process, a robust closed-loop approach is presented. The robust R2R-MPC models the uncertainty of the MSS process using a linear differential inclusion. As an analytical model of the MSS process is unavailable, the differential inclusion is identified from historical data. The R2R-MPC is posed as an optimization problem that computes incremental changes to the control input which minimize the worst-case material removal errors. This optimization-based controller is combined with a run-to-run controller to provide integral action that rejects constant disturbances and tracks constant reference removal rates. To demonstrate the efficacy of our robust R2R-MPC, we present simulation results which compare the presented controller with a conventional non-robust R2R.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2871-2876"},"PeriodicalIF":2.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890316","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 : 2024-12-09DOI: 10.1109/LCSYS.2024.3514822
Yizhou Gong;Yang Wang
Cooperative output regulation (COR) for multi-agent systems (MAS) has garnered significant attention due to its broad applications. This letter offers a fresh perspective on the COR problem for a class of heterogeneous, uncertain, linear SISO MAS facing two major challenges simultaneously: (1) the agents are highly uncertain and heterogeneous, and (2) communication is restricted to a directed spanning tree with only local information exchanged among agents. We propose a novel plug-and-play cooperative feedforward disturbance compensator that requires minimal prior knowledge of follower agents’ dynamics. In contrast to traditional methods, our compensator is fully distributed, adaptive, and highly robust to agent heterogeneity. It eliminates the need for system identification and handles large uncertainties without relying on typical assumptions such as minimum phase, identical dimensionality, or uniform relative degree across agents. Additionally, the compensator is designed for scalability, offering plug-and-play functionality that allows seamless addition or removal of agents without requiring controller redesign, provided the network maintains a spanning tree. Theoretical analysis and simulations demonstrate the compensator’s effectiveness in solving the COR problem across various scenarios.
{"title":"A Novel Plug-and-Play Cooperative Disturbance Compensator for Heterogeneous Uncertain Linear Multi-Agent Systems","authors":"Yizhou Gong;Yang Wang","doi":"10.1109/LCSYS.2024.3514822","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3514822","url":null,"abstract":"Cooperative output regulation (COR) for multi-agent systems (MAS) has garnered significant attention due to its broad applications. This letter offers a fresh perspective on the COR problem for a class of heterogeneous, uncertain, linear SISO MAS facing two major challenges simultaneously: (1) the agents are highly uncertain and heterogeneous, and (2) communication is restricted to a directed spanning tree with only local information exchanged among agents. We propose a novel plug-and-play cooperative feedforward disturbance compensator that requires minimal prior knowledge of follower agents’ dynamics. In contrast to traditional methods, our compensator is fully distributed, adaptive, and highly robust to agent heterogeneity. It eliminates the need for system identification and handles large uncertainties without relying on typical assumptions such as minimum phase, identical dimensionality, or uniform relative degree across agents. Additionally, the compensator is designed for scalability, offering plug-and-play functionality that allows seamless addition or removal of agents without requiring controller redesign, provided the network maintains a spanning tree. Theoretical analysis and simulations demonstrate the compensator’s effectiveness in solving the COR problem across various scenarios.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2811-2816"},"PeriodicalIF":2.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858888","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 : 2024-12-04DOI: 10.1109/LCSYS.2024.3511420
Xiupeng Chen;Nima Monshizadeh
Targeted interventions in games present a challenging problem due to the asymmetric information available to the regulator and the agents. This note addresses the problem of steering the actions of self-interested agents in quadratic network games towards a target action profile. A common starting point in the literature assumes prior knowledge of utility functions and/or network parameters. The goal of the results presented here is to remove this assumption and address scenarios where such a priori knowledge is unavailable. To this end, we design a data-driven dynamic intervention mechanism that relies solely on historical observations of agent actions and interventions. Additionally, we modify this mechanism to limit the amount of interventions, thereby considering budget constraints. Analytical convergence guarantees are provided for both mechanisms, and a numerical case study further demonstrates their effectiveness.
{"title":"Data-Driven Dynamic Intervention Design in Network Games","authors":"Xiupeng Chen;Nima Monshizadeh","doi":"10.1109/LCSYS.2024.3511420","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3511420","url":null,"abstract":"Targeted interventions in games present a challenging problem due to the asymmetric information available to the regulator and the agents. This note addresses the problem of steering the actions of self-interested agents in quadratic network games towards a target action profile. A common starting point in the literature assumes prior knowledge of utility functions and/or network parameters. The goal of the results presented here is to remove this assumption and address scenarios where such a priori knowledge is unavailable. To this end, we design a data-driven dynamic intervention mechanism that relies solely on historical observations of agent actions and interventions. Additionally, we modify this mechanism to limit the amount of interventions, thereby considering budget constraints. Analytical convergence guarantees are provided for both mechanisms, and a numerical case study further demonstrates their effectiveness.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2667-2672"},"PeriodicalIF":2.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810555","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 : 2024-12-04DOI: 10.1109/LCSYS.2024.3512332
Viet-Anh Le;Andreas A. Malikopoulos
In this letter, we consider the problem of coordinating traffic light systems and connected automated vehicles (CAVs) in mixed-traffic intersections. We aim to develop an optimization-based control framework that leverages both the coordination capabilities of CAVs at higher penetration rates and intelligent traffic management using traffic lights at lower penetration rates. Since the resulting optimization problem is a multi-agent mixed-integer quadratic program, we propose a penalization-enhanced maximum block improvement algorithm to solve the problem in a distributed manner. The proposed algorithm, under certain mild conditions, yields a feasible person-by-person optimal solution of the centralized problem. The performance of the control framework and the distributed algorithm is validated through simulations across various penetration rates and traffic volumes.
{"title":"Distributed Optimization for Traffic Light Control and Connected Automated Vehicle Coordination in Mixed-Traffic Intersections","authors":"Viet-Anh Le;Andreas A. Malikopoulos","doi":"10.1109/LCSYS.2024.3512332","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3512332","url":null,"abstract":"In this letter, we consider the problem of coordinating traffic light systems and connected automated vehicles (CAVs) in mixed-traffic intersections. We aim to develop an optimization-based control framework that leverages both the coordination capabilities of CAVs at higher penetration rates and intelligent traffic management using traffic lights at lower penetration rates. Since the resulting optimization problem is a multi-agent mixed-integer quadratic program, we propose a penalization-enhanced maximum block improvement algorithm to solve the problem in a distributed manner. The proposed algorithm, under certain mild conditions, yields a feasible person-by-person optimal solution of the centralized problem. The performance of the control framework and the distributed algorithm is validated through simulations across various penetration rates and traffic volumes.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2721-2726"},"PeriodicalIF":2.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821291","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 : 2024-12-04DOI: 10.1109/LCSYS.2024.3512333
Edgar I. Chavez-Aparicio;Hector M. Becerra;J. B. Hayet
In this letter, we propose a novel distributed vision-based formation control operating in the image space, with free-flying cameras in a three dimensional space as agents. Two controllers are proposed, both formulated in terms of a formation image error, without using a global reference frame nor requiring the estimation of the 3D pose between agents. The proposed formation scheme allows flexibility in defining the desired formation, without constraining it to planar formations, for example. We give formal stability guarantees based on Lyapunov analysis and evaluate our approach in simulations under a variety of initial and desired conditions, numbers of agents and inter-agent connectivity.
{"title":"A Novel Consensus-Based Formation Control Scheme in the Image Space","authors":"Edgar I. Chavez-Aparicio;Hector M. Becerra;J. B. Hayet","doi":"10.1109/LCSYS.2024.3512333","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3512333","url":null,"abstract":"In this letter, we propose a novel distributed vision-based formation control operating in the image space, with free-flying cameras in a three dimensional space as agents. Two controllers are proposed, both formulated in terms of a formation image error, without using a global reference frame nor requiring the estimation of the 3D pose between agents. The proposed formation scheme allows flexibility in defining the desired formation, without constraining it to planar formations, for example. We give formal stability guarantees based on Lyapunov analysis and evaluate our approach in simulations under a variety of initial and desired conditions, numbers of agents and inter-agent connectivity.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2769-2774"},"PeriodicalIF":2.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858923","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}
We formulate a stochastic zero-sum game over continuous-time dynamics to analyze the competition between the attacker, who tries to covertly misguide the vehicle to an unsafe region, versus the detector, who tries to detect the attack signal based on the observed trajectory of the vehicle. Based on Girsanov’s theorem and the generalized Neyman-Pearson lemma, we show that a constant bias injection attack as the attacker’s strategy and a likelihood ratio test as the detector’s strategy constitute the unique saddle point of the game. We also derive the first-order and the second-order exponents of the type II error as a function of the data length.
{"title":"Covert Vehicle Misguidance and Its Detection: A Hypothesis Testing Game Over Continuous-Time Dynamics","authors":"Takashi Tanaka;Kenji Sawada;Yohei Watanabe;Mitsugu Iwamoto","doi":"10.1109/LCSYS.2024.3511398","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3511398","url":null,"abstract":"We formulate a stochastic zero-sum game over continuous-time dynamics to analyze the competition between the attacker, who tries to covertly misguide the vehicle to an unsafe region, versus the detector, who tries to detect the attack signal based on the observed trajectory of the vehicle. Based on Girsanov’s theorem and the generalized Neyman-Pearson lemma, we show that a constant bias injection attack as the attacker’s strategy and a likelihood ratio test as the detector’s strategy constitute the unique saddle point of the game. We also derive the first-order and the second-order exponents of the type II error as a function of the data length.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2889-2894"},"PeriodicalIF":2.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890152","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 : 2024-12-04DOI: 10.1109/LCSYS.2024.3511429
Hao Wang;Adityaya Dhande;Somil Bansal
Autonomous systems have witnessed a rapid increase in their capabilities, but it remains a challenge for them to perform tasks both effectively and safely. The fact that performance and safety can sometimes be competing objectives renders the cooptimization between them difficult. One school of thought is to treat this cooptimization as a constrained optimal control problem with a performance-oriented objective function and safety as a constraint. However, solving this constrained optimal control problem for general nonlinear systems remains challenging. In this letter, we use the general framework of constrained optimal control, but given the safety state constraint, we convert it into an equivalent control constraint, resulting in a state and time-dependent control-constrained optimal control problem. This equivalent optimal control problem can readily be solved using the dynamic programming principle. We show the corresponding value function is a viscosity solution of a certain Hamilton-Jacobi-Bellman Partial Differential Equation (HJB-PDE). Furthermore, we demonstrate the effectiveness of our method with a two-dimensional case study, and the experiment shows that the controller synthesized using our method consistently outperforms the baselines, both in safety and performance. The implementation of the case study can be found on the project website ( https://github.com/haowwang/cooptimize_safety_performance ).
{"title":"Cooptimizing Safety and Performance With a Control-Constrained Formulation","authors":"Hao Wang;Adityaya Dhande;Somil Bansal","doi":"10.1109/LCSYS.2024.3511429","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3511429","url":null,"abstract":"Autonomous systems have witnessed a rapid increase in their capabilities, but it remains a challenge for them to perform tasks both effectively and safely. The fact that performance and safety can sometimes be competing objectives renders the cooptimization between them difficult. One school of thought is to treat this cooptimization as a constrained optimal control problem with a performance-oriented objective function and safety as a constraint. However, solving this constrained optimal control problem for general nonlinear systems remains challenging. In this letter, we use the general framework of constrained optimal control, but given the safety state constraint, we convert it into an equivalent control constraint, resulting in a state and time-dependent control-constrained optimal control problem. This equivalent optimal control problem can readily be solved using the dynamic programming principle. We show the corresponding value function is a viscosity solution of a certain Hamilton-Jacobi-Bellman Partial Differential Equation (HJB-PDE). Furthermore, we demonstrate the effectiveness of our method with a two-dimensional case study, and the experiment shows that the controller synthesized using our method consistently outperforms the baselines, both in safety and performance. The implementation of the case study can be found on the project website (\u0000<uri>https://github.com/haowwang/cooptimize_safety_performance</uri>\u0000).","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2739-2744"},"PeriodicalIF":2.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859187","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 : 2024-12-04DOI: 10.1109/LCSYS.2024.3511995
Davide Tebaldi;Roberto Zanasi
The applications of the partial fraction decomposition in control and systems engineering are several. In this letter, we use the partial fraction decomposition to address the pole variation rate problem, namely to study the rate of variation of the system poles when the control parameter changes and when the system is subject to variations of its own parameters, which has led to the proposal of a new algorithm for the construction of the root locus. The new algorithm is proven to be much more efficient in terms of execution time than the dedicated MATLAB function, while providing the same output results.
{"title":"Applications of the Partial Fraction Decomposition to Pole Variation Rate for Root Locus Construction","authors":"Davide Tebaldi;Roberto Zanasi","doi":"10.1109/LCSYS.2024.3511995","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3511995","url":null,"abstract":"The applications of the partial fraction decomposition in control and systems engineering are several. In this letter, we use the partial fraction decomposition to address the pole variation rate problem, namely to study the rate of variation of the system poles when the control parameter changes and when the system is subject to variations of its own parameters, which has led to the proposal of a new algorithm for the construction of the root locus. The new algorithm is proven to be much more efficient in terms of execution time than the dedicated MATLAB function, while providing the same output results.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2925-2930"},"PeriodicalIF":2.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10778273","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976174","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 : 2024-12-04DOI: 10.1109/LCSYS.2024.3511407
Mohamed Serry;Jun Liu
Analyzing and certifying the stability and attractivity of nonlinear systems is a topic of ongoing research interest that has been extensively investigated by control theorists and engineers for many years. However, accurately estimating domains of attraction for nonlinear systems remains a challenging task, where existing estimation methods tend to be conservative or limited to low-dimensional systems. In this letter, we propose an iterative approach to accurately underapproximate safe (state-constrained) domains of attraction for general discrete-time autonomous nonlinear systems. Our approach relies on implicit representations of safe backward reachable sets of initial safe regions of attraction, where such initial regions can be easily constructed using, e.g., quadratic Lyapunov functions. The iterations of our approach are monotonic (in the sense of set inclusion), converging to the safe domain of attraction. Each iteration results in a safe region of attraction, represented as a sublevel set, that underapproximates the safe domain of attraction. The sublevel set representations of the resulting regions of attraction can be efficiently utilized in verifying the inclusion of given points of interest in the safe domain of attraction. We illustrate our approach through two numerical examples, involving two- and four-dimensional nonlinear systems.
{"title":"Underapproximating Safe Domains of Attraction for Discrete-Time Systems Using Implicit Representations of Backward Reachable Sets","authors":"Mohamed Serry;Jun Liu","doi":"10.1109/LCSYS.2024.3511407","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3511407","url":null,"abstract":"Analyzing and certifying the stability and attractivity of nonlinear systems is a topic of ongoing research interest that has been extensively investigated by control theorists and engineers for many years. However, accurately estimating domains of attraction for nonlinear systems remains a challenging task, where existing estimation methods tend to be conservative or limited to low-dimensional systems. In this letter, we propose an iterative approach to accurately underapproximate safe (state-constrained) domains of attraction for general discrete-time autonomous nonlinear systems. Our approach relies on implicit representations of safe backward reachable sets of initial safe regions of attraction, where such initial regions can be easily constructed using, e.g., quadratic Lyapunov functions. The iterations of our approach are monotonic (in the sense of set inclusion), converging to the safe domain of attraction. Each iteration results in a safe region of attraction, represented as a sublevel set, that underapproximates the safe domain of attraction. The sublevel set representations of the resulting regions of attraction can be efficiently utilized in verifying the inclusion of given points of interest in the safe domain of attraction. We illustrate our approach through two numerical examples, involving two- and four-dimensional nonlinear systems.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2709-2714"},"PeriodicalIF":2.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821176","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 : 2024-12-02DOI: 10.1109/LCSYS.2024.3509960
Yi Lu;Roberto Guglielmi
We provide a sufficient condition to ensure turnpike for general nonlinear control systems. The main result of this letter exploits the connection between turnpike and the hyperbolicity of the corresponding optimality system and combines it with the theory of Algebraic Riccati Equations with indefinite source terms. We validate our sufficient condition with an example that does not satisfy previous sufficient conditions for turnpike.
{"title":"Turnpike in Nonlinear Optimal Control Problems With Indefinite Hamiltonian","authors":"Yi Lu;Roberto Guglielmi","doi":"10.1109/LCSYS.2024.3509960","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3509960","url":null,"abstract":"We provide a sufficient condition to ensure turnpike for general nonlinear control systems. The main result of this letter exploits the connection between turnpike and the hyperbolicity of the corresponding optimality system and combines it with the theory of Algebraic Riccati Equations with indefinite source terms. We validate our sufficient condition with an example that does not satisfy previous sufficient conditions for turnpike.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2691-2696"},"PeriodicalIF":2.4,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821250","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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