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Active Learning-Based Control for Resiliency of Uncertain Systems Under DoS Attacks 基于主动学习的不确定系统DoS攻击弹性控制
IF 2.4
IEEE Control Systems Letters Pub Date : 2024-12-26 DOI: 10.1109/LCSYS.2024.3522953
Sayan Chakraborty;Weinan Gao;Kyriakos G. Vamvoudakis;Zhong-Ping Jiang
{"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}
引用次数: 0
Projected Forward Gradient-Guided Frank-Wolfe Algorithm via Variance Reduction 基于方差缩减的投影前向梯度引导Frank-Wolfe算法
IF 2.4
IEEE Control Systems Letters Pub Date : 2024-12-26 DOI: 10.1109/LCSYS.2024.3523243
Mohammadreza Rostami;Solmaz S. Kia
{"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}
引用次数: 0
Control of a Noncooperative Positive Nonlinear System by Augmented Positive Linear System Regulation
IF 2.4
IEEE Control Systems Letters Pub Date : 2024-12-26 DOI: 10.1109/LCSYS.2024.3522944
Guanyun Liu;Amor A. Menezes
{"title":"Control of a Noncooperative Positive Nonlinear System by Augmented Positive Linear System Regulation","authors":"Guanyun Liu;Amor A. Menezes","doi":"10.1109/LCSYS.2024.3522944","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3522944","url":null,"abstract":"Positive systems, which are systems whose states are always non-negative, can have both positive linear and positive nonlinear approximations that are valid dynamical models in a prescribed domain. When a linearization of a nonlinear system in a domain near an operating point is equivalent to another linear system representation, a reference-tracking controller for that linear system should also achieve reference-tracking control of the nonlinear system in that domain. Here, we show that only if a linearized positive nonlinear system (PNS) is a positive system (i.e., the PNS is cooperative) will a reference-tracking controller for an equivalent positive linear system realization achieve similar results on the nonlinear system. For an example noncooperative PNS of human blood coagulation, where a published reference-tracking controller assumed a positive linear plant, we develop feedforward and feedback controllers that augment the prior controller to overcome noncooperativity and similarly control the positive nonlinear model.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3303-3308"},"PeriodicalIF":2.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184227","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}
引用次数: 0
Stochastic Data-Driven Predictive Control: Chance-Constraint Satisfaction With Identified Multi-Step Predictors 随机数据驱动的预测控制：识别多步预测器的机会约束满足
IF 2.4
IEEE Control Systems Letters Pub Date : 2024-12-26 DOI: 10.1109/LCSYS.2024.3523238
Haldun Balim;Andrea Carron;Melanie N. Zeilinger;Johannes Köhler
{"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}
引用次数: 0
Closed-Loop Analysis of ADMM-Based Suboptimal Linear Model Predictive Control 基于admm的次优线性模型预测控制闭环分析
IF 2.4
IEEE Control Systems Letters Pub Date : 2024-12-26 DOI: 10.1109/LCSYS.2024.3523241
Anusha Srikanthan;Aren Karapetyan;Vijay Kumar;Nikolai Matni
{"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}
引用次数: 0
Coactive Preference-Guided Multi-Objective Bayesian Optimization: An Application to Policy Learning in Personalized Plasma Medicine 协同偏好导向的多目标贝叶斯优化：在个性化血浆医疗政策学习中的应用
IF 2.4
IEEE Control Systems Letters Pub Date : 2024-12-25 DOI: 10.1109/LCSYS.2024.3521965
Ketong Shao;Ankush Chakrabarty;Ali Mesbah;Diego Romeres
{"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}
引用次数: 0
Two-Agent Noncooperative Dynamical Systems With Quadratic Vector-Valued Payoff Functions and Weak Pareto Improvement
IF 2.4
IEEE Control Systems Letters Pub Date : 2024-12-25 DOI: 10.1109/LCSYS.2024.3522596
Zehui Guo;Tomohisa Hayakawa
{"title":"Two-Agent Noncooperative Dynamical Systems With Quadratic Vector-Valued Payoff Functions and Weak Pareto Improvement","authors":"Zehui Guo;Tomohisa Hayakawa","doi":"10.1109/LCSYS.2024.3522596","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3522596","url":null,"abstract":"Definition of the weak Pareto improvement and the trap of weak Pareto improvement are given for noncooperative dynamical systems with vector-valued payoff functions. Specifically, we develop an incentive mechanism that satisfies sustainable budget constraint. We assume that there is a system manager who is authorized to design the incentive functions in order to maximize the social welfare of her choice. Specifically, the socially maximum state is predicated on the weighted sum of all the payoff functions. It turns out that depending on the choice of the design parameters, we may observe that some of the agents can be trapped in a weak Pareto improvement even though the system trajectory is weakly Pareto improving. Our results is a generalized version of the results for scalar-valued payoff functions.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3327-3332"},"PeriodicalIF":2.4,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184226","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}
引用次数: 0
Computationally Efficient Dual Mode Model Predictive Control to Ensure Safe Charging of Lithium-Ion Batteries 保证锂离子电池安全充电的计算高效双模模型预测控制
IF 2.4
IEEE Control Systems Letters Pub Date : 2024-12-23 DOI: 10.1109/LCSYS.2024.3522217
Suchita Undare;Kiana Karami;M. Scott Trimboli
{"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}
引用次数: 0
Parameter Identifiability and Reduction for Smooth and Nonsmooth Differential-Algebraic Equation Systems 光滑与非光滑微分代数方程组的参数可辨识性与约简
IF 2.4
IEEE Control Systems Letters Pub Date : 2024-12-23 DOI: 10.1109/LCSYS.2024.3521427
Hesham Abdelfattah;Peter Stechlinski;Sameh A. Eisa
{"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}
引用次数: 0
On the Stability of Consensus Control Under Rotational Ambiguities 旋转模糊条件下共识控制的稳定性
IF 2.4
IEEE Control Systems Letters Pub Date : 2024-12-23 DOI: 10.1109/LCSYS.2024.3521358
Zhonggang Li;Changheng Li;Raj Thilak Rajan
{"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}
引用次数: 0
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