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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
Parameter Tuning for Optimal Control of Switched Systems With Applications in Hypersonic Vehicles 高超声速飞行器切换系统最优控制参数整定
IF 2.4
IEEE Control Systems Letters Pub Date : 2024-12-23 DOI: 10.1109/LCSYS.2024.3521188
Shunan Yin;Ayush Rai;Shaoshuai Mou
{"title":"Parameter Tuning for Optimal Control of Switched Systems With Applications in Hypersonic Vehicles","authors":"Shunan Yin;Ayush Rai;Shaoshuai Mou","doi":"10.1109/LCSYS.2024.3521188","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3521188","url":null,"abstract":"Optimal control of switched systems (OCSS) is of great importance since they have significant practical implementation. This letter aims to tackle the problem of adapting OCSS to additional objective functions. We propose an algorithm to enable a tunable OCSS to adjust its parameters dynamically with respect to an additional loss function in a bi-level framework. At the higher level, the algorithm utilizes gradient descent to minimize this additional objective function while simultaneously addressing an optimal control problem at the lower level. By differentiating the maximum principle for the optimal control of switched systems, gradient computation is achieved by solving an auxiliary initial value problem. Besides theoretical analysis, the algorithm’s effectiveness is also numerically demonstrated by optimal control problems of a hypersonic vehicle with a combined-power engine.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3063-3068"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962845","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
Online Data-Driven Control of Nonlinear Systems Using Semidefinite Programming 非线性系统的半定规划在线数据驱动控制
IF 2.4
IEEE Control Systems Letters Pub Date : 2024-12-23 DOI: 10.1109/LCSYS.2024.3521645
Augusto Bozza;Tim Martin;Graziana Cavone;Raffaele Carli;Mariagrazia Dotoli;Frank Allgöwer
{"title":"Online Data-Driven Control of Nonlinear Systems Using Semidefinite Programming","authors":"Augusto Bozza;Tim Martin;Graziana Cavone;Raffaele Carli;Mariagrazia Dotoli;Frank Allgöwer","doi":"10.1109/LCSYS.2024.3521645","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3521645","url":null,"abstract":"This letter proposes a novel Data-Driven (DD) method for controlling unknown input-affine nonlinear systems. First, we estimate the system dynamics from noisy data offline through Subspace Identification of Nonlinear Dynamics. Then, at each time step during runtime, we exploit this estimation to deduce a feedback-linearization control law that robustly regulates all the systems consistent with the data. Notably, the control law is derived by solving a Semidefinite Programming (SDP) online. Moreover, closed-loop stability is ensured by constraining a Lyapunov function to descend in each time step using a linear-matrix-inequality representation. Unlike related DD control approaches for nonlinear systems based on SDP, our approach does not require any approximation of the nonlinear dynamics, while requiring the knowledge of a library of candidate basis functions. Finally, we validate our theoretical contributions by simulations for stabilization and tracking, outperforming another DD literature-inspired controller.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3189-3194"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10812702","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938119","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}
引用次数: 0
Safe Constraint Learning for Reference Governor Implementation in Constrained Linear Systems 约束线性系统中参考调速器实现的安全约束学习
IF 2.4
IEEE Control Systems Letters Pub Date : 2024-12-23 DOI: 10.1109/LCSYS.2024.3521191
Miguel Castroviejo-Fernandez;Ilya Kolmanovsky
{"title":"Safe Constraint Learning for Reference Governor Implementation in Constrained Linear Systems","authors":"Miguel Castroviejo-Fernandez;Ilya Kolmanovsky","doi":"10.1109/LCSYS.2024.3521191","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3521191","url":null,"abstract":"An approach to safe and fast online learning of constraints for a continuous-time linear system subject to linear inequality constraints is developed, assuming that the number of constraints is known and measurements of the constraint signals are available. During the identification phase, a constant reference command input is applied for the duration of an epoch and constraint measurements are collected. Based on these measurements, the set of feasible constraint parameters is refined using set-membership learning techniques. The reference command value is selected so that it minimizes the worst-case uncertainty in the parameters after one epoch while safety is ensured through the use of appropriately defined safe sets. The characterization of safe sets is shown to reduce to a finite set of linear inequality constraints. A numerical case study is reported for the proposed algorithm.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3117-3122"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962825","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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