IEEE open journal of control systems最新文献

Optimistic Algorithms for Safe Linear Bandits Under General Constraints 一般约束下安全线性强盗的乐观算法

IEEE open journal of control systems Pub Date : 2025-04-07 DOI: 10.1109/OJCSYS.2025.3558118

Spencer Hutchinson;Arghavan Zibaie;Ramtin Pedarsani;Mahnoosh Alizadeh

{"title":"Optimistic Algorithms for Safe Linear Bandits Under General Constraints","authors":"Spencer Hutchinson;Arghavan Zibaie;Ramtin Pedarsani;Mahnoosh Alizadeh","doi":"10.1109/OJCSYS.2025.3558118","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3558118","url":null,"abstract":"The stochastic linear bandit problem has emerged as a fundamental building-block in machine learning and control, and a realistic model for many applications. By equipping this classical problem with safety constraints, the <italic>safe linear bandit problem further broadens its relevance to safety-critical applications. However, most existing algorithms for safe linear bandits only consider <italic>linear constraints, making them inadequate for many real-world applications, which often have non-linear constraints. To alleviate this limitation, we study the problem of safe linear bandits under general (non-linear) constraints. Under a novel constraint regularity condition that is weaker than convexity, we give two algorithms with <inline-formula><tex-math>$tilde{mathcal {O}}(d sqrt{T})$</tex-math></inline-formula> regret. We then give efficient implementations of these algorithms for several specific settings. Lastly, we give simulation results demonstrating the effectiveness of our algorithms in choosing dynamic pricing signals for a demand response problem under distribution power flow constraints.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"103-116"},"PeriodicalIF":0.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10950393","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896165","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

Adaptive Actor-Critic Based Optimal Regulation for Drift-Free Nonlinear Systems 基于自适应因子评价的无漂移非线性系统最优调节

IEEE open journal of control systems Pub Date : 2025-03-18 DOI: 10.1109/OJCSYS.2025.3552999

Ashwin P. Dani;Shubhendu Bhasin

{"title":"Adaptive Actor-Critic Based Optimal Regulation for Drift-Free Nonlinear Systems","authors":"Ashwin P. Dani;Shubhendu Bhasin","doi":"10.1109/OJCSYS.2025.3552999","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3552999","url":null,"abstract":"In this paper, a continuous-time adaptive actor-critic reinforcement learning (RL) controller is developed for drift-free uncertain nonlinear systems. Practical examples of such systems are image-based visual servoing (IBVS) and wheeled mobile robots (WMR), where the system dynamics include a parametric uncertainty in the control effectiveness matrix with no drift term. The uncertainty in the input term poses a challenge when developing a continuous-time RL controller using existing methods. This paper presents an actor-critic/synchronous policy iteration (PI)-based RL controller with a newly derived constrained concurrent learning (CCL)-based parameter update law for estimating the unknown parameters of the linearly parametrized control effectiveness matrix. The parameter update law ensures that the parameters do not converge to <inline-formula><tex-math>$zero$</tex-math></inline-formula>, avoiding possible loss of stabilization. An infinite-horizon value function minimization objective is achieved by regulating the current states to the desired with near-optimal control efforts. The proposed controller guarantees closed-loop stability, and simulation results in the presence of noise validate the proposed theory using IBVS and WMR examples.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"117-129"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10932715","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072925","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

Online Learning of Stabilizing Controllers Using Noisy Input-Output Data and Prior Knowledge 基于噪声输入输出数据和先验知识的稳定控制器在线学习

IEEE open journal of control systems Pub Date : 2025-03-15 DOI: 10.1109/OJCSYS.2025.3570578

Nariman Niknejad;Farnaz Adib Yaghmaie;Hamidreza Modares

{"title":"Online Learning of Stabilizing Controllers Using Noisy Input-Output Data and Prior Knowledge","authors":"Nariman Niknejad;Farnaz Adib Yaghmaie;Hamidreza Modares","doi":"10.1109/OJCSYS.2025.3570578","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3570578","url":null,"abstract":"This paper presents online prior-knowledge-based data-driven approaches for verifying stability and learning a stabilizing dynamic controller for linear stochastic input-output systems. The system is modeled in an autoregressive exogenous (ARX) framework to accommodate cases where states are not fully observable. A key challenge addressed in this article is online stabilizing open-loop unstable systems, where collecting sufficient data for controller learning is impractical due to the risk of failure. To mitigate this, the proposed method integrates uncertain prior knowledge, derived from system physics, with limited available data. Inspired by set-membership system identification, the prior knowledge set is dynamically updated as new data becomes available, reducing conservatism over time. Unlike traditional approaches, this method bypasses explicit system identification, directly designing controllers based on current knowledge and data. A connection between ARX models and behavior theory is established, providing necessary and sufficient stability conditions using strict lossy <italic>S-Lemma. Quadratic difference forms serve as a framework for Lyapunov functions, and robust dynamic controllers are synthesized via linear matrix inequalities. The methodology is validated through simulations, including an unstable scalar system visualizing the integration of prior knowledge and data, and a rotary inverted pendulum demonstrating controller effectiveness in a nonlinear, unstable setting.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"156-171"},"PeriodicalIF":0.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11005422","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323194","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

Relationship Between the Number of Agents and Sparse Observability Index 智能体数量与稀疏可观察性指标的关系

IEEE open journal of control systems Pub Date : 2025-03-07 DOI: 10.1109/OJCSYS.2025.3567867

T. Shinohara;T. Namerikawa

{"title":"Relationship Between the Number of Agents and Sparse Observability Index","authors":"T. Shinohara;T. Namerikawa","doi":"10.1109/OJCSYS.2025.3567867","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3567867","url":null,"abstract":"The state estimation problem in the presence of malicious sensor attacks is commonly referred to as a secure state estimation problem. Central to addressing this problem is the concept of the sparse observability index, defined as the largest integer <inline-formula><tex-math>$ delta$</tex-math></inline-formula> for which the system remains observable after the removal of any <inline-formula><tex-math>$delta$</tex-math></inline-formula> sensors. This index plays a critical role in quantifying the resilience of the system, as a higher <inline-formula><tex-math>$delta$</tex-math></inline-formula> enables unique state reconstruction despite the presence of more compromised sensors. In this study, for undirected multi-agent systems consisting of <inline-formula><tex-math>$ n$</tex-math></inline-formula> agents, we analyze the relationship between the number of agents <inline-formula><tex-math>$ n$</tex-math></inline-formula> and the sparse observability index <inline-formula><tex-math>$ delta$</tex-math></inline-formula> for effective secure state estimation. In particular, we consider four typical graph structures: path, cycle, complete, and complete bipartite graphs. Our analysis reveals that <inline-formula><tex-math>$delta$</tex-math></inline-formula> does not increase monotonically with <inline-formula><tex-math>$n$</tex-math></inline-formula>, and that resilience is intricately tied to the underlying network structure. Notably, we demonstrate that the system exhibits enhanced resilience when the number of agents <inline-formula><tex-math>$n$</tex-math></inline-formula> is a prime number, although the specifics of this relationship vary depending on the graph topology.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"144-155"},"PeriodicalIF":0.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10989748","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170955","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

Precision Cylinder Gluing With Uncertainty-Aware MPC-Enhanced DDPG 具有不确定意识的mpc -增强型DDPG的精密气缸粘接

IEEE open journal of control systems Pub Date : 2025-03-01 DOI: 10.1109/OJCSYS.2025.3566323

Liangshun Wu;Junsuo Qu

引用次数: 0

Robustness to Modeling Errors in Risk-Sensitive Markov Decision Problems With Markov Risk Measures 使用马尔可夫风险度量的风险敏感马尔可夫决策问题中建模错误的鲁棒性

IEEE open journal of control systems Pub Date : 2025-02-04 DOI: 10.1109/OJCSYS.2025.3538267

Shiping Shao;Abhishek Gupta

{"title":"Robustness to Modeling Errors in Risk-Sensitive Markov Decision Problems With Markov Risk Measures","authors":"Shiping Shao;Abhishek Gupta","doi":"10.1109/OJCSYS.2025.3538267","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3538267","url":null,"abstract":"We consider risk-sensitive Markov decision processes (MDPs), where the MDP model is influenced by a parameter which takes values in a compact metric space. These situations arise when the underlying dynamics of the system depend on parameters that drifts over time. For example, mass of a vehicle depends on the number of passengers in the vehicle, which may change from one trip to another. Similarly, the energy demand of a building depends on the local weather, which changes every hour of the day. We identify sufficient conditions under which small perturbations in the model parameters lead to small changes in the optimal value function and optimal policy. This is achieved by establishing the continuity of the value function with respect to the parameters. A direct consequence of this result is that an optimal policy under a specific parameter remains near-optimal if the parameter is perturbed slightly. Implications of the results for data-driven decision-making, decision-making with preference uncertainty, and systems with changing noise distributions are discussed.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"70-82"},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10872799","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570694","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

Data-Driven Mobile Health: System Identification and Hybrid Model Predictive Control to Deliver Personalized Physical Activity Interventions 数据驱动的移动健康：系统识别和混合模型预测控制，以提供个性化的身体活动干预

IEEE open journal of control systems Pub Date : 2025-02-04 DOI: 10.1109/OJCSYS.2025.3538263

Mohamed El Mistiri;Owais Khan;César A. Martin;Eric Hekler;Daniel E. Rivera

{"title":"Data-Driven Mobile Health: System Identification and Hybrid Model Predictive Control to Deliver Personalized Physical Activity Interventions","authors":"Mohamed El Mistiri;Owais Khan;César A. Martin;Eric Hekler;Daniel E. Rivera","doi":"10.1109/OJCSYS.2025.3538263","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3538263","url":null,"abstract":"The integration of control systems principles in behavioral medicine involves developing interventions that can be personalized to foster healthy behaviors, such as meaningful and consistent engagement in physical activity. In this paper, system identification and hybrid model predictive control are applied to design individualized behavioral interventions using the <italic>control optimization trial (COT) framework. The paper details the multiple stages of a COT, from experimental design in system identification to controller implementation, and demonstrates its efficacy using participant data from <italic>Just Walk, an intervention that promotes walking behavior in sedentary adults. Mixed partitioning of estimation and validation data is applied to estimate ARX models for an illustrative participant, selecting the model with the best performance over a weighted norm balancing predictive ability with overall data fit. This model serves as the internal model in a three-degree-of-freedom Kalman filter-based Hybrid Model Predictive Controller (3DoF-KF HMPC) that provides “ambitious but doable” goals for initiation and maintenance phases of the physical activity intervention. Performance and robustness in a closed-loop setting are evaluated via both nominal and Monte Carlo simulation; the latter confirms the inherent robustness properties of the controller under plant-model mismatch. These results serve as proof of concept for the COT approach, which is currently being evaluated with human participants in the clinical trial <italic>YourMove (R01CA244777, NCT05598996).","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"83-102"},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10872807","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645315","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

Erratum to “Learning to Boost the Performance of Stable Nonlinear Systems” “学习提高稳定非线性系统的性能”的勘误

IEEE open journal of control systems Pub Date : 2025-02-03 DOI: 10.1109/OJCSYS.2025.3529361

Luca Furieri;Clara Lucía Galimberti;Giancarlo Ferrari-Trecate

{"title":"Erratum to “Learning to Boost the Performance of Stable Nonlinear Systems”","authors":"Luca Furieri;Clara Lucía Galimberti;Giancarlo Ferrari-Trecate","doi":"10.1109/OJCSYS.2025.3529361","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3529361","url":null,"abstract":"This addresses errors in [1]. Due to a production error, Figs. 4, 5, 6, 8, and 9 are not rendering correctly in the article PDF. The correct figures are as follows. Figure 4. Mountains—Closed-loop trajectories before training (left) and after training (middle and right) over 100 randomly sampled initial conditions marked with $circ$. Snapshots taken at time-instants τ. Colored (gray) lines show the trajectories in [0, τi] ([τi, ∞)). Colored balls (and their radius) represent the agents (and their size for collision avoidance). Figure 5. Mountains—Closed-loop trajectories after 25%, 50% and 75% of the total training whose closed-loop trajectory is shown in Fig. 4. Even if the performance can be further optimized, stability is always guaranteed. Figure 6. Mountains—Closed-loop trajectories after training. (Left and middle) Controller tested over a system with mass uncertainty (-10% and +10%, respectively). (Right) Trained controller with safety promotion through (45). Training initial conditions marked with $circ$. Snapshots taken at time-instants τ. Colored (gray) lines show the trajectories in [0, τi] ([τi, ∞)). Colored balls (and their radius) represent the agents (and their size for collision avoidance). Figure 8. Mountains—Closed-loop trajectories when using the online policy given by (48). Snapshots of three trajectories starting at different test initial conditions. Figure 9. Mountains—Three different closed-loop trajectories after training a REN controller without ${mathcal{L}}_{2}$ stability guarantees over 100 randomly sampled initial conditions marked with $circ$. Colored (gray) lines show the trajectories in (after) the training time interval.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"53-53"},"PeriodicalIF":0.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106755","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

Generalizing Robust Control Barrier Functions From a Controller Design Perspective 从控制器设计的角度推广鲁棒控制屏障函数

IEEE open journal of control systems Pub Date : 2025-01-13 DOI: 10.1109/OJCSYS.2025.3529364

Anil Alan;Tamas G. Molnar;Aaron D. Ames;Gábor Orosz

引用次数: 0

2024 Index IEEE Open Journal of Control Systems Vol. 3 2024索引IEEE控制系统开放杂志卷3

IEEE open journal of control systems Pub Date : 2025-01-10 DOI: 10.1109/OJCSYS.2025.3528596

引用次数: 0