Automatica最新文献_第10页

A non-linear non-Gaussian filtering framework based on the Gaussian noise model jump assumption 基于高斯噪声模型跳变假设的非线性非高斯滤波框架

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-05-08 DOI: 10.1016/j.automatica.2025.112360

Jiacheng He , Bei Peng , Gang Wang

引用次数: 0

On decentralized computation of the leader’s strategy in bi-level games 双层博弈中领导者策略的分散计算

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-05-08 DOI: 10.1016/j.automatica.2025.112352

Marko Maljkovic , Gustav Nilsson , Nikolas Geroliminis

{"title":"On decentralized computation of the leader’s strategy in bi-level games","authors":"Marko Maljkovic , Gustav Nilsson , Nikolas Geroliminis","doi":"10.1016/j.automatica.2025.112352","DOIUrl":"10.1016/j.automatica.2025.112352","url":null,"abstract":"<div><div>Motivated by the omnipresence of hierarchical structures in many real-world applications, this study delves into the intricate realm of bi-level games, with a specific focus on exploring local Stackelberg equilibria as a solution concept. While existing literature offers various methods tailored to specific game structures featuring one leader and multiple followers, a comprehensive framework providing formal convergence guarantees appears to be lacking. Drawing inspiration from sensitivity results for nonlinear programs and guided by the imperative to maintain scalability and preserve agent privacy, we propose a decentralized approach based on the projected gradient descent with the Armijo stepsize rule. By the virtue of the Implicit Function Theorem, we establish convergence to a local Stackelberg equilibrium for a broad class of bi-level games. Moreover, for quadratic aggregative Stackelberg games, we also introduce a decentralized warm-start procedure based on the consensus alternating direction method of multipliers addressing the initialization issues reported in our previous work. Finally, we provide empirical validation through two case studies in smart mobility, showcasing the effectiveness of our general method in handling general convex constraints, and the effectiveness of its extension in tackling initialization issues.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"178 ","pages":"Article 112352"},"PeriodicalIF":4.8,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923443","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}

引用次数: 0

Time-varying convex optimization with O(n) computational complexity 计算复杂度为O(n)的时变凸优化

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-05-06 DOI: 10.1016/j.automatica.2025.112355

Mohammadreza Rostami, Solmaz S. Kia

{"title":"Time-varying convex optimization with O(n) computational complexity","authors":"Mohammadreza Rostami, Solmaz S. Kia","doi":"10.1016/j.automatica.2025.112355","DOIUrl":"10.1016/j.automatica.2025.112355","url":null,"abstract":"<div><div>In this article, we consider the problem of unconstrained time-varying convex optimization, where the cost function changes with time. We provide an in-depth technical analysis of the problem and argue why discretizing and freezing the cost at each time step and taking finite steps towards the minimizer is not the best tracking solution for this problem. We propose a set of algorithms that by taking into account the temporal variation of the cost aim to reduce the tracking error of the time-varying minimizer of the problem. The main contribution of our work is that our proposed algorithms only require the first-order derivatives of the cost function with respect to the decision variable. This approach significantly reduces computational cost compared to the existing algorithms, which use the inverse of the Hessian of the cost. Specifically, the proposed algorithms reduce the computational cost from <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>3</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> to <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></mrow></math></span> per timestep, where <span><math><mi>n</mi></math></span> is the size of the decision variable. Avoiding the inverse of the Hessian also makes our algorithms applicable to non-convex optimization problems. We refer to these algorithms as <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></mrow></math></span>-algorithms. These <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></mrow></math></span>-algorithms are designed to solve the problem for different scenarios based on the available temporal information about the cost. We illustrate our results through various examples, including the solution of a model predictive control problem framed as a convex optimization problem with a streaming time-varying cost function.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"178 ","pages":"Article 112355"},"PeriodicalIF":4.8,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143911865","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}

引用次数: 0

Inverse supervised learning of controller tuning rules 控制器整定规则的逆监督学习

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-05-06 DOI: 10.1016/j.automatica.2025.112356

Braghadeesh Lakshminarayanan , Federico Dettú , Cristian R. Rojas , Simone Formentin

引用次数: 0

Filtering homogeneous observer for linear MIMO system 线性MIMO系统的滤波齐次观测器

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-05-06 DOI: 10.1016/j.automatica.2025.112357

Xubin Ping , Konstantin Zimenko , Andrey Polyakov , Denis Efimov

引用次数: 0

Backstepping neural operators for 2 × 2 hyperbolic PDEs 2 × 2双曲偏微分方程的反演神经算子

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-05-05 DOI: 10.1016/j.automatica.2025.112351

Shanshan Wang , Mamadou Diagne , Miroslav Krstic

{"title":"Backstepping neural operators for 2 × 2 hyperbolic PDEs","authors":"Shanshan Wang , Mamadou Diagne , Miroslav Krstic","doi":"10.1016/j.automatica.2025.112351","DOIUrl":"10.1016/j.automatica.2025.112351","url":null,"abstract":"<div><div>Deep neural network approximation of nonlinear operators, commonly referred to as DeepONet, has proven capable of approximating PDE backstepping designs in which a single Goursat-form PDE governs a single feedback gain function. In boundary control of coupled hyperbolic PDEs, coupled Goursat-form PDEs govern two or more gain kernels — a structure unaddressed thus far with DeepONet. In this contribution, we open the subject of approximating systems of gain kernel PDEs by considering a counter-convecting 2 × 2 hyperbolic system whose backstepping boundary controller and observer gains are the solutions to 2 × 2 kernel PDE systems in Goursat form. We establish the continuity of the mapping from (a total of five) functional coefficients of the plant to the kernel PDEs solutions, prove the existence of an arbitrarily close DeepONet approximation to the kernel PDEs, and ensure that the DeepONet-based approximated gains guarantee stabilization when replacing the exact backstepping gain kernel functions. Taking into account anti-collocated boundary actuation and sensing, our <span><math><msup><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span><em>-globally-exponentially stabilizing (GES)</em> control law requires the deep learning of both the controller and the observer gains. Moreover, the encoding of the feedback law into DeepONet ensures <em>semi-global practical exponential stability (SG-PES),</em> as established in our result. The neural operators (NOs) speed up the computation of both controller and observer gains by multiple orders of magnitude. Its theoretically proved stabilizing capability is demonstrated through simulations.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"178 ","pages":"Article 112351"},"PeriodicalIF":4.8,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904037","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}

引用次数: 0

Mean-square input-output stability and asymptotic stationarity of networked control systems with random transmission delays 具有随机传输延迟的网络控制系统的均方输入输出稳定性和渐近平稳性

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-05-03 DOI: 10.1016/j.automatica.2025.112339

Jieying Lu , Junhui Li , Weizhou Su

引用次数: 0

Learning for predictive control: A Dual Gaussian Process approach 预测控制的学习：双高斯过程方法

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-05-02 DOI: 10.1016/j.automatica.2025.112316

Yuhan Liu , Pengyu Wang , Roland Tóth

{"title":"Learning for predictive control: A Dual Gaussian Process approach","authors":"Yuhan Liu , Pengyu Wang , Roland Tóth","doi":"10.1016/j.automatica.2025.112316","DOIUrl":"10.1016/j.automatica.2025.112316","url":null,"abstract":"<div><div>An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian Process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based Model Predictive Control (MPC) strategy that enables efficient use of online learning based predictive control without the danger of catastrophic forgetting. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learnt knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. A novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation without a “dictionary” update and re-computation of the Gram matrix at each time step. Effectiveness of the proposed strategy is demonstrated via numerical simulations.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"177 ","pages":"Article 112316"},"PeriodicalIF":4.8,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894462","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}

引用次数: 0

Event-triggered distributed predictive control for multi-agent systems with stability constraints 具有稳定性约束的多智能体系统的事件触发分布式预测控制

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-05-02 DOI: 10.1016/j.automatica.2025.112358

Dongdong Qin , Chongjin Ong , Andong Liu , Wen-an Zhang , Li Yu

{"title":"Event-triggered distributed predictive control for multi-agent systems with stability constraints","authors":"Dongdong Qin , Chongjin Ong , Andong Liu , Wen-an Zhang , Li Yu","doi":"10.1016/j.automatica.2025.112358","DOIUrl":"10.1016/j.automatica.2025.112358","url":null,"abstract":"<div><div>This article investigates the event-triggered distributed predictive control problem for multi-agent systems, where each subsystem is subject to bounded disturbances and input constraints. Compared with existing works, we derive a less conservative event-triggering mechanism for each subsystem that does not involve neighbors’ information by leveraging stability constraints. This approach alleviates energy consumption and communication load in resource-constrained multi-agent systems. In our proposed framework, each subsystem solves the distributed optimization problem and transmits its information to its neighbors only when a certain level of control performance cannot be guaranteed according to the designed triggering condition. Moreover, a dynamic event-triggering mechanism is introduced by relaxing the requirements for the Lyapunov function candidate, thereby achieving longer inter-execution times between successive triggering instants. Theoretical analyses are developed to guarantee feasibility and closed-loop stability. Finally, numerical simulation examples are provided to verify the effectiveness of the proposed algorithms.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"178 ","pages":"Article 112358"},"PeriodicalIF":4.8,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900076","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}

引用次数: 0

Asymptotic inversion of linear systems: A constructive causal state-space approach 线性系统的渐近反演：一种建设性的因果状态空间方法

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-04-30 DOI: 10.1016/j.automatica.2025.112354

Hamza Benadada, Michael Di Loreto, Damien Ébérard, Paolo Massioni

引用次数: 0