AutomaticaPub Date : 2024-11-26DOI: 10.1016/j.automatica.2024.111995
Tianzhi Li, Jinzhi Wang
{"title":"Variational unscented Kalman filter on matrix Lie groups","authors":"Tianzhi Li, Jinzhi Wang","doi":"10.1016/j.automatica.2024.111995","DOIUrl":"10.1016/j.automatica.2024.111995","url":null,"abstract":"<div><div>In this paper, several estimation algorithms called the variational unscented Kalman filters (UKF-Vs) are proposed for matrix Lie groups. The proposed filters are inspired by the unscented Kalman filter in Euclidean space and they exhibit advantages over conventional methods, as the prediction step and the measurement update step are established on the Lie algebra and its dual space, which therefore avoids direct operations on highly nonlinear Lie group configuration spaces. Correspondingly, the proposed UKF-Vs exhibit significant improvements in terms of the estimation error and mean square error. This also makes it possible to construct a computationally efficient geometric integrator for the filtering dynamics. The obtained formulation is independent of the nonlinear Lie group state-space, and it sheds light on fully predicting and updating on the Lie algebra and its dual which are endowed with vector space structures. In particular, these formulations can avoid singularities or the well-known gimbal lock in the attitude estimation problem. Furthermore, the stochastic stability of the proposed filters is studied. The performances of the proposed filters are demonstrated for the satellite attitude estimation problem, which is an important benchmark from a control perspective. Numerical results show that the proposed UKF-Vs keep less computational complexity and perform significantly high accuracy compared with two unscented Kalman filters on Lie groups.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"172 ","pages":"Article 111995"},"PeriodicalIF":4.8,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AutomaticaPub Date : 2024-11-26DOI: 10.1016/j.automatica.2024.112002
Jiangjiang Cheng , Ge Chen , Wenjun Mei , Francesco Bullo
{"title":"Multidimensional opinion dynamics with heterogeneous bounded confidences and random interactions","authors":"Jiangjiang Cheng , Ge Chen , Wenjun Mei , Francesco Bullo","doi":"10.1016/j.automatica.2024.112002","DOIUrl":"10.1016/j.automatica.2024.112002","url":null,"abstract":"<div><div>This paper introduces a heterogeneous multidimensional bounded confidence (BC) opinion dynamics with random pairwise interactions, whereby each pair of agents accesses each other’s opinions with a specific probability. This revised model is motivated by the observation that the standard Hegselmann–Krause (HK) dynamics requires unrealistic all-to-all interactions at certain configurations. For this randomized BC opinion dynamics, regardless of initial opinions and positive confidence bounds, we show that the agents’ states converge to fixed final opinions in finite time almost surely and that the convergence rate follows a negative exponential distribution in mean square. Furthermore, we establish sufficient conditions for the heterogeneous BC opinion dynamics with random interactions to achieve consensus in finite time.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"172 ","pages":"Article 112002"},"PeriodicalIF":4.8,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AutomaticaPub Date : 2024-11-26DOI: 10.1016/j.automatica.2024.111975
Jiang Long , Wei Wang , Changyun Wen , Jiangshuai Huang , Yangming Guo
{"title":"Distributed adaptive leaderless output consensus of uncertain nonlinear multi-agent systems with heterogenous system orders","authors":"Jiang Long , Wei Wang , Changyun Wen , Jiangshuai Huang , Yangming Guo","doi":"10.1016/j.automatica.2024.111975","DOIUrl":"10.1016/j.automatica.2024.111975","url":null,"abstract":"<div><div>This paper is concerned with the leaderless output consensus control problem for uncertain nonlinear multi-agent systems with mismatched unknown parameters. Different from currently available results, the considered agents are allowed to have heterogenous and arbitrary system orders. To solve such consensus problem, a novel distributed reference system, whose output serves as a local reference signal, is first designed for each agent. Then, under directed interaction topology condition, a fully distributed leaderless output consensus control scheme is proposed based on adaptive backstepping technique, by using only relative outputs of neighboring agents. It is shown that with the proposed control scheme, all the closed-loop signals are globally uniformly bounded and all the agents’ outputs can reach consensus. Finally, an application example is utilized to verify the effectiveness of the proposed control scheme.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"172 ","pages":"Article 111975"},"PeriodicalIF":4.8,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AutomaticaPub Date : 2024-11-26DOI: 10.1016/j.automatica.2024.112004
Tenglong Kang , Carla Seatzu , Zhiwu Li , Alessandro Giua
{"title":"A joint diagnoser approach for diagnosability of discrete event systems under attack","authors":"Tenglong Kang , Carla Seatzu , Zhiwu Li , Alessandro Giua","doi":"10.1016/j.automatica.2024.112004","DOIUrl":"10.1016/j.automatica.2024.112004","url":null,"abstract":"<div><div>This paper investigates the problem of diagnosing the occurrence of a fault event in a discrete event system (DES) subject to malicious attacks. We consider a DES monitored by an operator through the perceived sensor observations. It is assumed that an attacker can tamper with the sensor observations, and the system operator is not aware of the attacker’s presence at the beginning. We propose a stealthy joint diagnoser (SJD) that (i) describes all possible stealthy attacks (i.e., undiscovered by the operator) in a given attack scenario; (ii) records the joint diagnosis state, i.e., the diagnosis state of the attacker consistent with the original observation and the diagnosis state of the operator consistent with the corrupted observation. The SJD is used for diagnosability verification under attack. From the attacker’s point of view, we present two levels of stealthy attackers: one only temporarily degrades the diagnosis state of the operator, and the other permanently causes damage to the diagnosis state of the operator, thereby resulting in a violation of diagnosability. Finally, necessary and sufficient conditions for the existence of the two levels of attackers are presented.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"172 ","pages":"Article 112004"},"PeriodicalIF":4.8,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AutomaticaPub Date : 2024-11-26DOI: 10.1016/j.automatica.2024.111990
Yiwen Chen , Guoguang Wen , Ahmed Rahmani , Zhaoxia Peng , Jun Jiang , Tingwen Huang
{"title":"Resilient stepped transmission and control for nonlinear systems against DoS attacks","authors":"Yiwen Chen , Guoguang Wen , Ahmed Rahmani , Zhaoxia Peng , Jun Jiang , Tingwen Huang","doi":"10.1016/j.automatica.2024.111990","DOIUrl":"10.1016/j.automatica.2024.111990","url":null,"abstract":"<div><div>In this paper, we focus on developing a resilient stepped transmission scheme for nonlinear networked systems suffering from Denial-of-Service (DoS) attacks. Instead of employing static periodic sampling, the proposed acknowledgement-based adaptive sampler follows a resilient stepped algorithm to dynamically adjust the sampling period based on the absence or presence of DoS attacks. The dynamic sampling period is demonstrated to approach the boundary sampling interval to better adapt to the vulnerable environment, where the boundary sampling interval is the largest sampling period to ensure the boundedness of successful transmission interval in conventional periodic sampling scheme. Two impulsive observer-based control structures are presented to ensure the asymptotic stability of the equilibrium of systems, in which the latter one can better cope with the amplification of observer error in the presence of DoS. Numerical simulations are given finally to substantiate the validity of theoretical results.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"172 ","pages":"Article 111990"},"PeriodicalIF":4.8,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AutomaticaPub Date : 2024-11-26DOI: 10.1016/j.automatica.2024.111924
Zhenhui Xu , Bing-Chang Wang , Tielong Shen
{"title":"Mean field LQG social optimization: A reinforcement learning approach","authors":"Zhenhui Xu , Bing-Chang Wang , Tielong Shen","doi":"10.1016/j.automatica.2024.111924","DOIUrl":"10.1016/j.automatica.2024.111924","url":null,"abstract":"<div><div>This paper presents a novel model-free method to solve linear quadratic Gaussian mean field social control problems in the presence of multiplicative noise. The objective is to achieve a social optimum by solving two algebraic Riccati equations (AREs) and determining a mean field (MF) state, both without requiring prior knowledge of individual system dynamics for all agents. In the proposed approach, we first employ integral reinforcement learning techniques to develop two model-free iterative equations that converge to solutions for the stochastic ARE and the induced indefinite ARE respectively. Then, the MF state is approximated, either through the Monte Carlo method with the obtained gain matrices or through the system identification with the measured data. Notably, a unified state and input samples collected from a single agent are used in both iterations and identification procedure, making the method more computationally efficient and scalable. Finally, a numerical example is given to demonstrate the effectiveness of the proposed algorithm.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"172 ","pages":"Article 111924"},"PeriodicalIF":4.8,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AutomaticaPub Date : 2024-11-26DOI: 10.1016/j.automatica.2024.111991
Jing Chen , Yawen Mao , Dongqing Wang , Min Gan , Quanmin Zhu , Feng Liu
{"title":"Reduced-order identification methods: Hierarchical algorithm or variable elimination algorithm","authors":"Jing Chen , Yawen Mao , Dongqing Wang , Min Gan , Quanmin Zhu , Feng Liu","doi":"10.1016/j.automatica.2024.111991","DOIUrl":"10.1016/j.automatica.2024.111991","url":null,"abstract":"<div><div>Reduced-order identification algorithms are usually used in machine learning and big data technologies, where the large-scale systems widely exist. For large-scale system identification, traditional least squares algorithm involves high-order matrix inverse calculation, while traditional gradient descent algorithm has slow convergence rates. The reduced-order algorithm proposed in this paper has some advantages over the previous work: (1) via sequential partitioning of the parameter vector, the calculation of the inverse of a high-order matrix can be reduced to low-order matrix inverse calculations; (2) has a better conditioned information matrix than that of the gradient descent algorithm, thus has faster convergence rates; (3) its convergence rates can be increased by using the Aitken acceleration method, therefore the reduced-order based Aitken algorithm is at least quadratic convergent and has no limitation on the step-size. The properties of the reduced-order algorithm are also given. Simulation results demonstrate the effectiveness of the proposed algorithm.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"172 ","pages":"Article 111991"},"PeriodicalIF":4.8,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AutomaticaPub Date : 2024-11-22DOI: 10.1016/j.automatica.2024.111989
Jie Lian , Shuang An , Dong Wang
{"title":"Graph-based necessary and sufficient conditions for exponential stability of switched positive systems with marginally stable subsystems","authors":"Jie Lian , Shuang An , Dong Wang","doi":"10.1016/j.automatica.2024.111989","DOIUrl":"10.1016/j.automatica.2024.111989","url":null,"abstract":"<div><div>This paper derives necessary and sufficient (N&S) conditions for the exponential stability of discrete-time switched positive linear systems under transfer-restricted switching. The transfer-restricted switching property is characterized by a switching digraph, and the structural properties for subsystems are characterized by a novel class of state component digraphs. Combining with the two properties, some joint path conditions involving the sum and product matrices of multiple subsystems respectively are presented as N&S conditions based on weak common linear co-positive Lyapunov functions (weak-CLCLFs). The presented conditions allow all subsystems to be marginally stable rather than asymptotically stable. Finally, a simulation example is provided to show the effectiveness and superiority of the proposed method.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"171 ","pages":"Article 111989"},"PeriodicalIF":4.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AutomaticaPub Date : 2024-11-22DOI: 10.1016/j.automatica.2024.111992
Ya Zheng Dang , Jie Sun , Kok Lay Teo
{"title":"Two inertial proximal coordinate algorithms for a family of nonsmooth and nonconvex optimization problems","authors":"Ya Zheng Dang , Jie Sun , Kok Lay Teo","doi":"10.1016/j.automatica.2024.111992","DOIUrl":"10.1016/j.automatica.2024.111992","url":null,"abstract":"<div><div>The inertial proximal method is extended to minimize the sum of a series of separable nonconvex and possibly nonsmooth objective functions and a smooth nonseparable function (possibly nonconvex). Here, we propose two new algorithms. The first one is an inertial proximal coordinate subgradient algorithm, which updates the variables by employing the proximal subgradients of each separable function at the current point. The second one is an inertial proximal block coordinate method, which updates the variables by using the subgradients of the separable functions at the partially updated points. Global convergence is guaranteed under the Kurdyka–Łojasiewicz (KŁ) property and some additional mild assumptions. Convergence rate is derived based on the Łojasiewicz exponent. Two numerical examples are given to illustrate the effectiveness of the algorithms.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"171 ","pages":"Article 111992"},"PeriodicalIF":4.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AutomaticaPub Date : 2024-11-21DOI: 10.1016/j.automatica.2024.111996
Xiaodong He , Weijia Yao , Zhiyong Sun , Zhongkui Li
{"title":"A novel vector-field-based motion planning algorithm for 3D nonholonomic robots","authors":"Xiaodong He , Weijia Yao , Zhiyong Sun , Zhongkui Li","doi":"10.1016/j.automatica.2024.111996","DOIUrl":"10.1016/j.automatica.2024.111996","url":null,"abstract":"<div><div>This paper focuses on the motion planning for mobile robots in 3D, which are modeled by 6-DOF rigid body systems with nonholonomic kinematics constraints. We not only specify the target position, but also impose the requirement of the heading direction at the terminal time, which gives rise to a new and more challenging 3D motion planning problem. The proposed planning algorithm involves a novel velocity vector field (VF) over the workspace, and by following the VF, the robot can be navigated to the destination with the specified heading direction. In order to circumvent potential collisions with obstacles and other robots, a composite VF is designed by composing the navigation VF and an additional VF tangential to the boundary of the dangerous area. Moreover, we propose a priority-based algorithm to deal with the motion coupling issue among multiple robots. Finally, simulations are conducted to verify the theoretical results.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"172 ","pages":"Article 111996"},"PeriodicalIF":4.8,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}