IEEE open journal of control systems最新文献_第6页

An Efficient Solution to Optimal Motion Planning With Provable Safety and Convergence 具有可证明安全性和收敛性的最优运动规划高效解决方案

IEEE open journal of control systems Pub Date : 2024-03-18 DOI: 10.1109/OJCSYS.2024.3378055

PANAGIOTIS ROUSSEAS;Charalampos Bechlioulis;Kostas Kyriakopoulos

引用次数: 0

Self-Excited Dynamics of Discrete-Time Lur'e Systems With Affinely Constrained, Piecewise-C$^{1}$ Feedback Nonlinearities 具有 Affinely 约束、Piecewise-C$^{1}$ 反馈非线性的离散时间 Lur'e 系统的自激动力学

IEEE open journal of control systems Pub Date : 2024-03-16 DOI: 10.1109/OJCSYS.2024.3402050

Juan A. Paredes;Omran Kouba;Dennis S. Bernstein

{"title":"Self-Excited Dynamics of Discrete-Time Lur'e Systems With Affinely Constrained, Piecewise-C$^{1}$ Feedback Nonlinearities","authors":"Juan A. Paredes;Omran Kouba;Dennis S. Bernstein","doi":"10.1109/OJCSYS.2024.3402050","DOIUrl":"https://doi.org/10.1109/OJCSYS.2024.3402050","url":null,"abstract":"Self-excited systems (SES) arise in numerous applications, such as fluid-structure interaction, combustion, and biochemical systems. In support of system identification and digital control of SES, this paper analyzes discrete-time Lur'e systems with affinely constrained, piecewise-C\u0000<inline-formula><tex-math>$^{1}$</tex-math></inline-formula>\u0000 feedback nonlinearities. In particular, a novel feature of the discrete-time Lur'e system considered in this paper is the structural assumption that the linear dynamics possess a zero at 1. This assumption ensures that the Lur'e system have a unique equilibrium for each constant, exogenous input and prevents the system from having an additional equilibrium with a nontrivial domain of attraction. The main result provides sufficient conditions under which a discrete-time Lur'e system is self-excited in the sense that its response is 1) nonconvergent for almost all initial conditions, and 2) bounded for all initial conditions. Sufficient conditions for 1) include the instability and nonsingularity of the linearized, closed-loop dynamics at the unique equilibrium and their nonsingularity almost everywhere. Sufficient conditions for 2) include asymptotic stability of the linear dynamics of the Lur'e system and their feedback interconnection with linear mappings that correspond to the affine constraints that bound the nonlinearity, as well as the feasibility of a linear matrix inequality.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"214-224"},"PeriodicalIF":0.0,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10531633","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141304041","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

Model-Free Change Point Detection for Mixing Processes 混合过程的无模型变化点检测

IEEE open journal of control systems Pub Date : 2024-03-08 DOI: 10.1109/OJCSYS.2024.3398530

Hao Chen;Abhishek Gupta;Yin Sun;Ness Shroff

{"title":"Model-Free Change Point Detection for Mixing Processes","authors":"Hao Chen;Abhishek Gupta;Yin Sun;Ness Shroff","doi":"10.1109/OJCSYS.2024.3398530","DOIUrl":"https://doi.org/10.1109/OJCSYS.2024.3398530","url":null,"abstract":"This paper considers the change point detection problem under dependent samples. In particular, we provide performance guarantees for the MMD-CUSUM test under exponentially \u0000<inline-formula><tex-math>$alpha$</tex-math></inline-formula>\u0000, \u0000<inline-formula><tex-math>$beta$</tex-math></inline-formula>\u0000, and fast \u0000<inline-formula><tex-math>$phi$</tex-math></inline-formula>\u0000-mixing processes, which significantly expands its utility beyond the i.i.d. and Markovian cases used in previous studies. We obtain lower bounds for average-run-length (\u0000<inline-formula><tex-math>$ {mathtt {ARL}}$</tex-math></inline-formula>\u0000) and upper bounds for average-detection-delay (\u0000<inline-formula><tex-math>$ {mathtt {ADD}}$</tex-math></inline-formula>\u0000) in terms of the threshold parameter. We show that the MMD-CUSUM test enjoys the same level of performance as the i.i.d. case under fast \u0000<inline-formula><tex-math>$phi$</tex-math></inline-formula>\u0000-mixing processes. The MMD-CUSUM test also achieves strong performance under exponentially \u0000<inline-formula><tex-math>$alpha$</tex-math></inline-formula>\u0000/\u0000<inline-formula><tex-math>$beta$</tex-math></inline-formula>\u0000-mixing processes, which are significantly more relaxed than existing results. The MMD-CUSUM test statistic adapts to different settings without modifications, rendering it a completely data-driven, dependence-agnostic change point detection scheme. Numerical simulations are provided at the end to evaluate our findings.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"202-213"},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10522896","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141304042","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

Navigation Systems May Deteriorate Stability in Traffic Networks 导航系统可能会降低交通网络的稳定性

IEEE open journal of control systems Pub Date : 2024-03-06 DOI: 10.1109/OJCSYS.2024.3397270

Gianluca Bianchin;Fabio Pasqualetti

{"title":"Navigation Systems May Deteriorate Stability in Traffic Networks","authors":"Gianluca Bianchin;Fabio Pasqualetti","doi":"10.1109/OJCSYS.2024.3397270","DOIUrl":"10.1109/OJCSYS.2024.3397270","url":null,"abstract":"Advanced traffic navigation systems, which provide routing recommendations to drivers based on real-time congestion information, are nowadays widely adopted by roadway transportation users. Yet, the emerging effects on the traffic dynamics originating from the widespread adoption of these technologies have remained largely unexplored until now. In this paper, we propose a dynamic model where drivers imitate the path preferences of previous drivers, and we study the properties of its equilibrium points. Our model is a dynamic generalization of the classical \u0000<italic>traffic assignment framework</i>\u0000, and extends it by accounting for dynamics both in the path decision process and in the network's traffic flows. We show that, when travelers learn shortest paths by imitating other travelers, the overall traffic system benefits from this mechanism and transfers the maximum admissible amount of traffic demand. On the other hand, we demonstrate that, when the travel delay functions are not sufficiently steep or the rates at which drivers imitate previous travelers are not adequately chosen, the trajectories of the traffic system may fail to converge to an equilibrium point, thus compromising asymptotic stability. Illustrative numerical simulations combined with empirical data from highway sensors illustrate our findings.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"239-252"},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10520878","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141221343","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

Nonstationary Stochastic Bandits: UCB Policies and Minimax Regret 非平稳随机强盗：UCB 政策和最小遗憾

IEEE open journal of control systems Pub Date : 2024-03-05 DOI: 10.1109/OJCSYS.2024.3372929

Lai Wei;Vaibhav Srivastava

{"title":"Nonstationary Stochastic Bandits: UCB Policies and Minimax Regret","authors":"Lai Wei;Vaibhav Srivastava","doi":"10.1109/OJCSYS.2024.3372929","DOIUrl":"https://doi.org/10.1109/OJCSYS.2024.3372929","url":null,"abstract":"We study the nonstationary stochastic Multi-Armed Bandit (MAB) problem in which the distributions of rewards associated with arms are assumed to be time-varying and the total variation in the expected rewards is subject to a variation budget. The regret of a policy is defined by the difference in the expected cumulative reward obtained using the policy and using an oracle that selects the arm with the maximum mean reward at each time. We characterize the performance of the proposed policies in terms of the worst-case regret, which is the supremum of the regret over the set of reward distribution sequences satisfying the variation budget. We design Upper-Confidence Bound (UCB)-based policies with three different approaches, namely, periodic resetting, sliding observation window, and discount factor, and show that they are order-optimal with respect to the minimax regret, i.e., the minimum worst-case regret achieved by any policy. We also relax the sub-Gaussian assumption on reward distributions and develop robust versions of the proposed policies that can handle heavy-tailed reward distributions and maintain their performance guarantees.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"128-142"},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10460198","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342701","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

A Computationally-Efficient Data-Driven Safe Optimal Algorithm Through Control Merging 通过控制合并实现计算高效的数据驱动安全最优算法

IEEE open journal of control systems Pub Date : 2024-02-22 DOI: 10.1109/OJCSYS.2024.3368850

Marjan Khaledi;Bahare Kiumarsi

{"title":"A Computationally-Efficient Data-Driven Safe Optimal Algorithm Through Control Merging","authors":"Marjan Khaledi;Bahare Kiumarsi","doi":"10.1109/OJCSYS.2024.3368850","DOIUrl":"https://doi.org/10.1109/OJCSYS.2024.3368850","url":null,"abstract":"This article presents a proactive approach to resolving the conflict between safety and optimality for continuous-time (CT) safety-critical systems with unknown dynamics. The presented method guarantees safety and performance specifications by combining two controllers: a safe controller and an optimal controller. On the one hand, the safe controller is designed using only input and state data measurements and without requiring the state derivative data, which are typically required in data-driven control of CT systems. State derivative measurement is costly, and its approximation introduces noise to the system. On the other hand, the optimal controller is learned using a low-complexity one-shot optimization problem, which again does not rely on prior knowledge of the system dynamics and state derivative data. Compared to existing optimal control learning methods for CT systems, which are typically iterative, a one-shot optimization is considerably more sample-efficient and computationally efficient. The share of optimal and safe controllers in the overall control policy is obtained by solving a computationally efficient optimization problem involving a scalar variable in a data-driven manner. It is shown that the contribution of the safe controller dominates that of the optimal controller when the system's state is close to the safety boundaries, and this domination drops as the system trajectories move away from the safety boundaries. In this case, the optimal controller contributes more to the overall controller. The feasibility and stability of the proposed controller are shown. Finally, the simulation results show the efficacy of the proposed approach.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"118-127"},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10443513","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140328981","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

A Multiplex Approach Against Disturbance Propagation in Nonlinear Networks With Delays 在有延迟的非线性网络中对抗干扰传播的多重方法

IEEE open journal of control systems Pub Date : 2024-01-26 DOI: 10.1109/OJCSYS.2024.3359089

Shihao Xie;Giovanni Russo

引用次数: 0

Classification of Human Learning Stages via Kernel Distribution Embeddings 通过核分布嵌入对人类学习阶段进行分类

IEEE open journal of control systems Pub Date : 2024-01-01 DOI: 10.1109/OJCSYS.2023.3348704

Madeleine Shuhn-Tsuan Yuh;Kendric Ray Ortiz;Kylie Sue Sommer-Kohrt;Meeko Oishi;Neera Jain

{"title":"Classification of Human Learning Stages via Kernel Distribution Embeddings","authors":"Madeleine Shuhn-Tsuan Yuh;Kendric Ray Ortiz;Kylie Sue Sommer-Kohrt;Meeko Oishi;Neera Jain","doi":"10.1109/OJCSYS.2023.3348704","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3348704","url":null,"abstract":"Adaptive automation, automation which is responsive to the human's performance via the alteration of control laws or level of assistance, is an important tool for training humans to attain new skills when operating dynamical systems. When coupled with cognitive feedback, adaptive automation has the potential to further facilitate human training, but requires precise assessments of human progression through various learning stages. This is challenging because of the underlying dynamics, as well as the stochasticity inherent to human action. We propose a data-driven approach to assess learning stages in a complex quadrotor landing task that is responsive to stochastic, human-in-the-loop quadrotor dynamics. We represent each learning stage as a distribution of canonical trajectories for that learning stage, then employ kernel distribution embeddings in combination with a rule-based heuristic, to determine which canonical distribution a sample landing trajectory is closest to. We demonstrate our approach on experimental human subject data, and use our approach to evaluate the efficacy of cognitively-based adaptive automation designed to calibrate self-confidence. Our approach is more accurate than standard classification methods, such as nearest centroid assignment, which rely on metrics that are not inherently suited to analysis of trajectories of stochastic dynamical systems.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"102-117"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10378711","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139976167","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

2023 Index IEEE Open Journal of Control Systems Vol.2 2023 Index IEEE Open Journal of Control Systems Vol.2

IEEE open journal of control systems Pub Date : 2023-12-21 DOI: 10.1109/OJCSYS.2023.3345772

引用次数: 0

Human Trust in Robots: A Survey on Trust Models and Their Controls/Robotics Applications 人类对机器人的信任：信任模型及其控制/机器人应用调查

IEEE open journal of control systems Pub Date : 2023-12-20 DOI: 10.1109/OJCSYS.2023.3345090

Yue Wang;Fangjian Li;Huanfei Zheng;Longsheng Jiang;Maziar Fooladi Mahani;Zhanrui Liao

{"title":"Human Trust in Robots: A Survey on Trust Models and Their Controls/Robotics Applications","authors":"Yue Wang;Fangjian Li;Huanfei Zheng;Longsheng Jiang;Maziar Fooladi Mahani;Zhanrui Liao","doi":"10.1109/OJCSYS.2023.3345090","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3345090","url":null,"abstract":"Trust model is a topic that first gained interest in organizational studies and then human factors in automation. Thanks to recent advances in human-robot interaction (HRI) and human-autonomy teaming, human trust in robots has gained growing interest among researchers and practitioners. This article focuses on a survey of computational models of human-robot trust and their applications in robotics and robot controls. The motivation is to provide an overview of the state-of-the-art computational methods to quantify trust so as to provide feedback and situational awareness in HRI. Different from other existing survey papers on human-robot trust models, we seek to provide in-depth coverage of the trust model categorization, formulation, and analysis, with a focus on their utilization in robotics and robot controls. The paper starts with a discussion of the difference between human-robot trust with general agent-agent trust, interpersonal trust, and human trust in automation and machines. A list of impacting factors for human-robot trust and different trust measurement approaches, and their corresponding scales are summarized. We then review existing computational human-robot trust models and discuss the pros and cons of each category of models. These include performance-centric algebraic, time-series, Markov decision process (MDP)/Partially Observable MDP (POMDP)-based, Gaussian-based, and dynamic Bayesian network (DBN)-based trust models. Following the summary of each computational human-robot trust model, we examine its utilization in robot control applications, if any. We also enumerate the main limitations and open questions in this field and discuss potential future research directions.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"58-86"},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10366819","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139431049","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