Pub Date : 2024-12-26DOI: 10.1109/LCSYS.2024.3523244
Hanwen Cai;Weiyao Lan;Xiao Yu
This letter addresses the semi-global output regulation problem for continuous-time linear systems with input saturation and unknown dynamics. First, we employ a low-gain technique to design a state-feedback linear control law such that the control input operates within the linear region of the actuator. Then, taking it as the linear part, we construct a composite nonlinear feedback (CNF) control law, consisting of both linear and nonlinear parts, to improve the transient performance of the closed-loop system. Without requiring prior knowledge of the system dynamics or an initial stabilizing control policy, we propose a novel adaptive dynamic programming (ADP) learning algorithm. This algorithm learns both the linear part and the nonlinear part of the CNF control law using the same set of data. In addition, the algorithm uses single-layer filters, eliminating the need for integral operations during the learning process. Finally, the effectiveness of the proposed algorithm is demonstrated by an illustrative example.
{"title":"Data-Driven Composite Nonlinear Feedback Control for Semi-Global Output Regulation of Unknown Linear Systems With Input Saturation","authors":"Hanwen Cai;Weiyao Lan;Xiao Yu","doi":"10.1109/LCSYS.2024.3523244","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3523244","url":null,"abstract":"This letter addresses the semi-global output regulation problem for continuous-time linear systems with input saturation and unknown dynamics. First, we employ a low-gain technique to design a state-feedback linear control law such that the control input operates within the linear region of the actuator. Then, taking it as the linear part, we construct a composite nonlinear feedback (CNF) control law, consisting of both linear and nonlinear parts, to improve the transient performance of the closed-loop system. Without requiring prior knowledge of the system dynamics or an initial stabilizing control policy, we propose a novel adaptive dynamic programming (ADP) learning algorithm. This algorithm learns both the linear part and the nonlinear part of the CNF control law using the same set of data. In addition, the algorithm uses single-layer filters, eliminating the need for integral operations during the learning process. Finally, the effectiveness of the proposed algorithm is demonstrated by an illustrative example.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3225-3230"},"PeriodicalIF":2.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938061","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 : 2024-12-26DOI: 10.1109/LCSYS.2024.3522949
Ryoma Yasunaga;Yorie Nakahira;Yutaka Hori
Quantifying long-term risk in large-scale multi-agent systems is critical for ensuring safe operation. However, the high dimensionality of these systems and the rarity of risk events can make the required computations prohibitively expensive. To overcome this challenge, we introduce a graph-based representation and efficient risk quantification techniques tailored for stochastic multi-agent systems. A key technical innovation is a systematic approach to decompose the estimation problem of system-wide safety probabilities into smaller, lower-dimensional sub-systems with sub-safe sets. This decomposition leverages the graph Fourier basis of the agent interaction network, providing a natural and scalable representation. The safety probabilities for these sub-systems are derived as solutions to a set of low-dimensional partial differential equations (PDEs). The proposed decomposition enables existing risk quantification approaches but does so without an exponential increase in computational complexity with respect to the number of agents.
{"title":"Orthogonal Modal Representation in Long-Term Risk Quantification for Dynamic Multi-Agent Systems","authors":"Ryoma Yasunaga;Yorie Nakahira;Yutaka Hori","doi":"10.1109/LCSYS.2024.3522949","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3522949","url":null,"abstract":"Quantifying long-term risk in large-scale multi-agent systems is critical for ensuring safe operation. However, the high dimensionality of these systems and the rarity of risk events can make the required computations prohibitively expensive. To overcome this challenge, we introduce a graph-based representation and efficient risk quantification techniques tailored for stochastic multi-agent systems. A key technical innovation is a systematic approach to decompose the estimation problem of system-wide safety probabilities into smaller, lower-dimensional sub-systems with sub-safe sets. This decomposition leverages the graph Fourier basis of the agent interaction network, providing a natural and scalable representation. The safety probabilities for these sub-systems are derived as solutions to a set of low-dimensional partial differential equations (PDEs). The proposed decomposition enables existing risk quantification approaches but does so without an exponential increase in computational complexity with respect to the number of agents.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3177-3182"},"PeriodicalIF":2.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10816487","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938118","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 : 2024-12-26DOI: 10.1109/LCSYS.2024.3522953
Sayan Chakraborty;Weinan Gao;Kyriakos G. Vamvoudakis;Zhong-Ping Jiang
In this letter, we present an active learning-based control method for discrete-time linear systems with unknown parameters under denial-of-service (DoS) attacks. For any DoS duration parameter, using switching systems theory and adaptive dynamic programming, an active learning-based control technique is developed. A critical DoS average dwell-time is learned from online input-state data, guaranteeing stability of the equilibrium point of the closed-loop system in the presence of DoS attacks with average dwell-time greater than or equal to the critical DoS average dwell-time. The effectiveness of the proposed methodology is illustrated via a numerical example.
{"title":"Active Learning-Based Control for Resiliency of Uncertain Systems Under DoS Attacks","authors":"Sayan Chakraborty;Weinan Gao;Kyriakos G. Vamvoudakis;Zhong-Ping Jiang","doi":"10.1109/LCSYS.2024.3522953","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3522953","url":null,"abstract":"In this letter, we present an active learning-based control method for discrete-time linear systems with unknown parameters under denial-of-service (DoS) attacks. For any DoS duration parameter, using switching systems theory and adaptive dynamic programming, an active learning-based control technique is developed. A critical DoS average dwell-time is learned from online input-state data, guaranteeing stability of the equilibrium point of the closed-loop system in the presence of DoS attacks with average dwell-time greater than or equal to the critical DoS average dwell-time. The effectiveness of the proposed methodology is illustrated via a numerical example.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3297-3302"},"PeriodicalIF":2.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975936","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 : 2024-12-26DOI: 10.1109/LCSYS.2024.3523243
Mohammadreza Rostami;Solmaz S. Kia
This letter aims to enhance the use of the Frank-Wolfe (FW) algorithm for training deep neural networks. Similar to any gradient-based optimization algorithm, FW suffers from high computational and memory costs when computing gradients for DNNs. This letter introduces the application of the recently proposed projected forward gradient (Projected-FG) method to the FW framework, offering reduced computational cost similar to backpropagation and low memory utilization akin to forward propagation. Our results show that trivial application of the Projected-FG introduces non-vanishing convergence error due to the stochastic noise that the Projected-FG method introduces in the process. This noise results in an non-vanishing variance in the Projected-FG estimated gradient. To address this, we propose a variance reduction approach by aggregating historical Projected-FG directions. We demonstrate rigorously that this approach ensures convergence to the optimal solution for convex functions and to a stationary point for non-convex functions. Simulations demonstrate our results.
{"title":"Projected Forward Gradient-Guided Frank-Wolfe Algorithm via Variance Reduction","authors":"Mohammadreza Rostami;Solmaz S. Kia","doi":"10.1109/LCSYS.2024.3523243","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3523243","url":null,"abstract":"This letter aims to enhance the use of the Frank-Wolfe (FW) algorithm for training deep neural networks. Similar to any gradient-based optimization algorithm, FW suffers from high computational and memory costs when computing gradients for DNNs. This letter introduces the application of the recently proposed projected forward gradient (Projected-FG) method to the FW framework, offering reduced computational cost similar to backpropagation and low memory utilization akin to forward propagation. Our results show that trivial application of the Projected-FG introduces non-vanishing convergence error due to the stochastic noise that the Projected-FG method introduces in the process. This noise results in an non-vanishing variance in the Projected-FG estimated gradient. To address this, we propose a variance reduction approach by aggregating historical Projected-FG directions. We demonstrate rigorously that this approach ensures convergence to the optimal solution for convex functions and to a stationary point for non-convex functions. Simulations demonstrate our results.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3153-3158"},"PeriodicalIF":2.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938114","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 : 2024-12-26DOI: 10.1109/LCSYS.2024.3523238
Haldun Balim;Andrea Carron;Melanie N. Zeilinger;Johannes Köhler
We propose a novel data-driven stochastic model predictive control framework for uncertain linear systems with noisy output measurements. Our approach leverages multi-step predictors to efficiently propagate uncertainty, ensuring chance constraint satisfaction. In particular, we present a strategy to identify multi-step predictors and quantify the associated uncertainty using a surrogate (data-driven) state space model. Then, we utilize the derived distribution to formulate a constraint tightening that ensures chance constraint satisfaction despite the parametric uncertainty. A numerical example highlights the reduced conservatism of handling parametric uncertainty in the proposed method compared to state-of-the-art solutions.
{"title":"Stochastic Data-Driven Predictive Control: Chance-Constraint Satisfaction With Identified Multi-Step Predictors","authors":"Haldun Balim;Andrea Carron;Melanie N. Zeilinger;Johannes Köhler","doi":"10.1109/LCSYS.2024.3523238","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3523238","url":null,"abstract":"We propose a novel data-driven stochastic model predictive control framework for uncertain linear systems with noisy output measurements. Our approach leverages multi-step predictors to efficiently propagate uncertainty, ensuring chance constraint satisfaction. In particular, we present a strategy to identify multi-step predictors and quantify the associated uncertainty using a surrogate (data-driven) state space model. Then, we utilize the derived distribution to formulate a constraint tightening that ensures chance constraint satisfaction despite the parametric uncertainty. A numerical example highlights the reduced conservatism of handling parametric uncertainty in the proposed method compared to state-of-the-art solutions.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3249-3254"},"PeriodicalIF":2.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938552","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 : 2024-12-26DOI: 10.1109/LCSYS.2024.3523241
Anusha Srikanthan;Aren Karapetyan;Vijay Kumar;Nikolai Matni
Many practical applications of optimal control are subject to real-time computational constraints. When applying model predictive control (MPC) in these settings, respecting timing constraints is achieved by limiting the number of iterations of the optimization algorithm used to compute control actions at each time step, resulting in so-called suboptimal MPC. This letter proposes a suboptimal MPC scheme based on the alternating direction method of multipliers (ADMM). With a focus on the linear quadratic regulator problem with state and input constraints, we show how ADMM can be used to split the MPC problem into iterative updates of an unconstrained optimal control problem (with an analytical solution), and a dynamics-free feasibility step. We show that using a warm-start approach combined with enough iterations per time-step, yields an ADMM-based suboptimal MPC scheme which asymptotically stabilizes the system and maintains recursive feasibility.
{"title":"Closed-Loop Analysis of ADMM-Based Suboptimal Linear Model Predictive Control","authors":"Anusha Srikanthan;Aren Karapetyan;Vijay Kumar;Nikolai Matni","doi":"10.1109/LCSYS.2024.3523241","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3523241","url":null,"abstract":"Many practical applications of optimal control are subject to real-time computational constraints. When applying model predictive control (MPC) in these settings, respecting timing constraints is achieved by limiting the number of iterations of the optimization algorithm used to compute control actions at each time step, resulting in so-called suboptimal MPC. This letter proposes a suboptimal MPC scheme based on the alternating direction method of multipliers (ADMM). With a focus on the linear quadratic regulator problem with state and input constraints, we show how ADMM can be used to split the MPC problem into iterative updates of an unconstrained optimal control problem (with an analytical solution), and a dynamics-free feasibility step. We show that using a warm-start approach combined with enough iterations per time-step, yields an ADMM-based suboptimal MPC scheme which asymptotically stabilizes the system and maintains recursive feasibility.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3195-3200"},"PeriodicalIF":2.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938058","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}
The design of advanced learning- and optimization-based controllers requires selecting parameters that balance performance objectives and constraints. Bayesian optimization (BO) has proven effective for resource-efficient calibration of such controllers. Preference-guided BO incorporates user preferences to prioritize areas of interest, but it lacks a mechanism for users to specify desired outcomes directly. This letter introduces a user-centric framework for preference-guided BO, leveraging a novel knowledge-gradient based coactive acquisition function that allows users not only to select preferred outcomes but also also propose alternatives to guide exploration. To enable efficient implementation, we approximate the acquisition function, avoiding costly bilevel optimization. The approach is validated for control policy adaptation in personalized plasma medicine, where it outperforms standard preference-guided BO by effectively integrating user feedback to personalize treatment protocol.
{"title":"Coactive Preference-Guided Multi-Objective Bayesian Optimization: An Application to Policy Learning in Personalized Plasma Medicine","authors":"Ketong Shao;Ankush Chakrabarty;Ali Mesbah;Diego Romeres","doi":"10.1109/LCSYS.2024.3521965","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3521965","url":null,"abstract":"The design of advanced learning- and optimization-based controllers requires selecting parameters that balance performance objectives and constraints. Bayesian optimization (BO) has proven effective for resource-efficient calibration of such controllers. Preference-guided BO incorporates user preferences to prioritize areas of interest, but it lacks a mechanism for users to specify desired outcomes directly. This letter introduces a user-centric framework for preference-guided BO, leveraging a novel knowledge-gradient based coactive acquisition function that allows users not only to select preferred outcomes but also also propose alternatives to guide exploration. To enable efficient implementation, we approximate the acquisition function, avoiding costly bilevel optimization. The approach is validated for control policy adaptation in personalized plasma medicine, where it outperforms standard preference-guided BO by effectively integrating user feedback to personalize treatment protocol.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3081-3086"},"PeriodicalIF":2.4,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962850","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 : 2024-12-23DOI: 10.1109/LCSYS.2024.3522217
Suchita Undare;Kiana Karami;M. Scott Trimboli
Model predictive control (MPC) has emerged as a promising strategy for the control of lithium-ion batteries due mainly to its capability for real-time constraint handling. However, classical implementations of MPC cannot guarantee stability, thus limiting its practical application. In addition, classical linear MPC relies on the computation of a constrained quadratic program at every time step, the computation of which may become burdensome when long horizons and numerous constraints are involved. The present paper applies a “dual mode” variation of MPC which reduces the necessity of implementing a quadratic program and provides assured stability of operation, at the cost of introducing a degree of conservatism.
{"title":"Computationally Efficient Dual Mode Model Predictive Control to Ensure Safe Charging of Lithium-Ion Batteries","authors":"Suchita Undare;Kiana Karami;M. Scott Trimboli","doi":"10.1109/LCSYS.2024.3522217","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3522217","url":null,"abstract":"Model predictive control (MPC) has emerged as a promising strategy for the control of lithium-ion batteries due mainly to its capability for real-time constraint handling. However, classical implementations of MPC cannot guarantee stability, thus limiting its practical application. In addition, classical linear MPC relies on the computation of a constrained quadratic program at every time step, the computation of which may become burdensome when long horizons and numerous constraints are involved. The present paper applies a “dual mode” variation of MPC which reduces the necessity of implementing a quadratic program and provides assured stability of operation, at the cost of introducing a degree of conservatism.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3093-3098"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962811","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 : 2024-12-23DOI: 10.1109/LCSYS.2024.3521427
Hesham Abdelfattah;Peter Stechlinski;Sameh A. Eisa
We extend the sensitivity rank condition (SERC), which tests for identifiability of smooth input-output systems, to a broader class of systems. Particularly, we build on our recently developed lexicographic SERC (L-SERC) theory and methods to achieve an identifiability test for differential-algebraic equation (DAE) systems for the first time, including nonsmooth systems. Additionally, we develop a method to determine the identifiable and non-identifiable parameter sets. We show how this new theory can be used to establish a (non-local) parameter reduction procedure and we show how parameter estimation problems can be solved. We apply the new methods to problems in wind turbine power systems and glucose-insulin kinetics.
{"title":"Parameter Identifiability and Reduction for Smooth and Nonsmooth Differential-Algebraic Equation Systems","authors":"Hesham Abdelfattah;Peter Stechlinski;Sameh A. Eisa","doi":"10.1109/LCSYS.2024.3521427","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3521427","url":null,"abstract":"We extend the sensitivity rank condition (SERC), which tests for identifiability of smooth input-output systems, to a broader class of systems. Particularly, we build on our recently developed lexicographic SERC (L-SERC) theory and methods to achieve an identifiability test for differential-algebraic equation (DAE) systems for the first time, including nonsmooth systems. Additionally, we develop a method to determine the identifiable and non-identifiable parameter sets. We show how this new theory can be used to establish a (non-local) parameter reduction procedure and we show how parameter estimation problems can be solved. We apply the new methods to problems in wind turbine power systems and glucose-insulin kinetics.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3159-3164"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938115","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 : 2024-12-23DOI: 10.1109/LCSYS.2024.3521358
Zhonggang Li;Changheng Li;Raj Thilak Rajan
Consensus control of multiagent systems arises in various applications such as rendezvous and formation control. The input to these algorithms, e.g., the (relative) positions of neighboring agents need to be measured using various sensors. Recent works aim to reconstruct these positions, i.e., achieve localization using Euclidean distance measurements instead of displacements, for cost efficiency and scalability. However, this approach inherently introduces ambiguities, such as a rotation or a reflection, which can cause stability issues in practice without corrections by some anchors. In this letter, we conduct a thorough analysis of the stability of consensus control in the presence of localization-induced rotational ambiguities, in several scenarios including, e.g., proper and improper rotation, and the homogeneity of rotations. We give stability criteria and stability margin on the rotations, which are numerically verified with two traditional examples of consensus control.
{"title":"On the Stability of Consensus Control Under Rotational Ambiguities","authors":"Zhonggang Li;Changheng Li;Raj Thilak Rajan","doi":"10.1109/LCSYS.2024.3521358","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3521358","url":null,"abstract":"Consensus control of multiagent systems arises in various applications such as rendezvous and formation control. The input to these algorithms, e.g., the (relative) positions of neighboring agents need to be measured using various sensors. Recent works aim to reconstruct these positions, i.e., achieve localization using Euclidean distance measurements instead of displacements, for cost efficiency and scalability. However, this approach inherently introduces ambiguities, such as a rotation or a reflection, which can cause stability issues in practice without corrections by some anchors. In this letter, we conduct a thorough analysis of the stability of consensus control in the presence of localization-induced rotational ambiguities, in several scenarios including, e.g., proper and improper rotation, and the homogeneity of rotations. We give stability criteria and stability margin on the rotations, which are numerically verified with two traditional examples of consensus control.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3273-3278"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938064","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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