Automatica最新文献_第7页

Nonlinear voltage regulation of an auxiliary energy storage of a multiport interconnection 多端口互连辅助储能系统的非线性电压调节

IF 5.9 2区计算机科学

Automatica Pub Date : 2025-09-02 DOI: 10.1016/j.automatica.2025.112558

Felipe Morales , Rafael Cisneros , Romeo Ortega , Antonio Sanchez-Squella

{"title":"Nonlinear voltage regulation of an auxiliary energy storage of a multiport interconnection","authors":"Felipe Morales , Rafael Cisneros , Romeo Ortega , Antonio Sanchez-Squella","doi":"10.1016/j.automatica.2025.112558","DOIUrl":"10.1016/j.automatica.2025.112558","url":null,"abstract":"<div><div>Motivated by the explosive increase in the use of DC–DC converters due to the massification of renewable energies, in this article we propose a nonlinear voltage control to ensure power exchange in a multiport interconnected system, which consists of a bidirectional DC–DC converter and generating-storing devices. The converter topology under consideration is two-stage, composed of an interconnection of a buck with a boost converter. The control design for this system does not correspond to that in standard applications involving power converters. As it is known, the latter consists of finding a control law such that the closed-loop system has an asymptotically stable equilibrium point fulfilling the voltage regulation objectives. Instead, in this application, the state does not tend to an equilibrium value in order for the system to be regulated. The converter voltage is regulated at some desired setpoint whereas the other variables are only required to be bounded. To achieve a dynamic response that best adapts to changes in system demand and ensure stability over the defined wide operating range, we propose a novel control strategy that exploits the partially cascaded structure of the system. <em>Experimental</em> results validate our approach.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"182 ","pages":"Article 112558"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925936","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

Revisiting Lossless Convexification: Theoretical guarantees for discrete-time optimal control problems 再论无损凸化：离散时间最优控制问题的理论保证

IF 5.9 2区计算机科学

Automatica Pub Date : 2025-09-02 DOI: 10.1016/j.automatica.2025.112537

Dayou Luo , Kazuya Echigo , Behçet Açıkmeşe

{"title":"Revisiting Lossless Convexification: Theoretical guarantees for discrete-time optimal control problems","authors":"Dayou Luo , Kazuya Echigo , Behçet Açıkmeşe","doi":"10.1016/j.automatica.2025.112537","DOIUrl":"10.1016/j.automatica.2025.112537","url":null,"abstract":"<div><div>Lossless Convexification (LCvx) is a modeling approach that transforms a class of nonconvex optimal control problems, where nonconvexity primarily arises from control constraints, into convex problems through convex relaxations. These convex problems can be solved using polynomial-time numerical methods after discretization, which converts the original infinite-dimensional problem into a finite-dimensional one. However, existing LCvx theory is limited to continuous-time optimal control problems, as the equivalence between the relaxed convex problem and the original nonconvex problem holds only in continuous-time. This paper extends LCvx theory to discrete-time optimal control problems by classifying them into normal and long-horizon cases. For normal cases, after an arbitrarily small perturbation to the system dynamics (recursive equality constraints), applying the existing LCvx method to discrete-time problems results in optimal controls that meet the original nonconvex constraints at all but no more than <span><math><mrow><msub><mrow><mi>n</mi></mrow><mrow><mi>x</mi></mrow></msub><mo>−</mo><mn>1</mn></mrow></math></span> temporal grid points, where <span><math><msub><mrow><mi>n</mi></mrow><mrow><mi>x</mi></mrow></msub></math></span> is the state dimension. For long-horizon cases, the existing LCvx method fails, but we resolve this issue by integrating it with a bisection search, leveraging the continuity of the value function from the relaxed convex problem to achieve similar results as in normal cases. This paper strengthens the theoretical foundation of LCvx, extending the applicability of LCvx theory to discrete-time optimal control problems.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"183 ","pages":"Article 112537"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926767","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

Accelerated primal–dual methods for strongly convex objective functions in continuous and discrete time 连续和离散时间强凸目标函数的加速原对偶方法

IF 5.9 2区计算机科学

Automatica Pub Date : 2025-09-02 DOI: 10.1016/j.automatica.2025.112579

Xin He , Dong He , Ya-Ping Fang

{"title":"Accelerated primal–dual methods for strongly convex objective functions in continuous and discrete time","authors":"Xin He , Dong He , Ya-Ping Fang","doi":"10.1016/j.automatica.2025.112579","DOIUrl":"10.1016/j.automatica.2025.112579","url":null,"abstract":"<div><div>In this paper, we introduce a “second-order primal” + “first-order dual” continuous-time dynamic for linearly constrained optimization problems, where the objective function is <span><math><mi>μ</mi></math></span>-strongly convex. We consider a constant damping <span><math><mrow><mn>2</mn><msqrt><mrow><mi>μ</mi></mrow></msqrt></mrow></math></span> for the second-order ordinary differential equation in the primal variable, following Nesterov’s acceleration for strongly convex optimization. A positive constant scaling is applied to the primal variable, while a positive increasing scaling function is applied to the dual variable. We prove that the proposed dynamic achieves a fast convergence rate for both the objective residual and the feasibility violation, with the decay rate potentially reaching <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>e</mi></mrow><mrow><mo>−</mo><msqrt><mrow><mi>μ</mi></mrow></msqrt><mi>t</mi></mrow></msup><mo>)</mo></mrow></mrow></math></span>. Additionally, we show that the dynamic is robust under small perturbations. By discretizing the proposed continuous-time dynamic, we develop an accelerated linearized augmented Lagrangian method for strongly convex composite optimization with linear constraints, where the objective function has a nonsmooth + smooth composite structure. The proposed algorithm achieves a fast convergence rate that matches the one of the continuous-time dynamic. We also consider an inexact version of the proposed algorithm, which can be viewed as a discrete version of the perturbed continuous-time dynamic. Numerical results are provided to verify the practical performances.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"183 ","pages":"Article 112579"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926768","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

A characterization method of terminal ingredients for nonlinear MPC using value-based reinforcement learning 基于值强化学习的非线性MPC终端成分表征方法

IF 5.9 2区计算机科学

Automatica Pub Date : 2025-09-02 DOI: 10.1016/j.automatica.2025.112574

Jinghan Cui , Jinwu Gao , Xiangjie Liu , Yuqi Liu , Shuyou Yu

{"title":"A characterization method of terminal ingredients for nonlinear MPC using value-based reinforcement learning","authors":"Jinghan Cui , Jinwu Gao , Xiangjie Liu , Yuqi Liu , Shuyou Yu","doi":"10.1016/j.automatica.2025.112574","DOIUrl":"10.1016/j.automatica.2025.112574","url":null,"abstract":"<div><div>The stability of nonlinear model predictive control (MPC) relies significantly on stabilizing factors such as the terminal region and cost. A larger terminal region not only expands the region of attraction for the closed-loop system but also contributes to reducing online computation costs. However, existing methods in the literature often impose limitations on the degrees of freedom available for characterizing terminal ingredients. This limitation arises from the reliance on either a predetermined linear local controller or a preset control Lyapunov function. This paper introduces an innovative approach to terminal ingredient characterization leveraging value-based reinforcement learning (RL). This method provides ample degrees of freedom for expanding the terminal region. To achieve this, a deep neural network is employed to learn the parametric state value function, serving as the terminal cost for MPC. The local controller adopts a one-step MPC instead of a predetermined linear or nonlinear feedback controller. Subsequently, a terminal set sequence is constructed iteratively through the one-step set expansion. The proposed approach’s effectiveness is validated through simulations.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"183 ","pages":"Article 112574"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926770","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

Distributed stochastic constrained optimization with constant step-sizes via saddle-point dynamics 基于鞍点动力学的等步长分布随机约束优化

IF 5.9 2区计算机科学

Automatica Pub Date : 2025-09-02 DOI: 10.1016/j.automatica.2025.112575

Yi Huang , Shisheng Cui , Xianlin Zeng , Ziyang Meng

{"title":"Distributed stochastic constrained optimization with constant step-sizes via saddle-point dynamics","authors":"Yi Huang , Shisheng Cui , Xianlin Zeng , Ziyang Meng","doi":"10.1016/j.automatica.2025.112575","DOIUrl":"10.1016/j.automatica.2025.112575","url":null,"abstract":"<div><div>This paper considers distributed stochastic optimization problems over a multi-agent network, where each agent collaboratively minimizes the sum of individual expectation-valued cost functions subject to nonidentical set constraints. We first recast the distributed constrained optimization as a constrained saddle-point problem. Subsequently, two distributed stochastic algorithms via optimistic gradient descent ascent (SOGDA) and extragradient (SEG) methods are developed with constant step sizes, in which the variable sample-size technique is incorporated to reduce the variance of the sampled gradients. We present the explicit selection criteria of the constant step size, under which the developed algorithms achieve almost sure convergence to an optimal solution. Moreover, the convergence rate is <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mn>1</mn><mo>/</mo><mi>k</mi><mo>)</mo></mrow></mrow></math></span> for merely convex cost functions, which matches the optimal rate of its deterministic counterpart. Finally, a numerical example is provided to reflect the theoretical findings.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"183 ","pages":"Article 112575"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926766","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

Federated Cubic Regularized Newton Learning with sparsification-amplified differential privacy 稀疏化放大差分隐私的联邦三次正则牛顿学习

IF 5.9 2区计算机科学

Automatica Pub Date : 2025-09-02 DOI: 10.1016/j.automatica.2025.112531

Wei Huo , Changxin Liu , Kemi Ding , Karl Henrik Johansson , Ling Shi

引用次数: 0

Fixed-time learning for safe time-critical verification using reachability analysis 使用可达性分析进行安全的时间关键验证的固定时间学习

IF 5.9 2区计算机科学

Automatica Pub Date : 2025-09-01 DOI: 10.1016/j.automatica.2025.112528

Nick-Marios T. Kokolakis , Kyriakos G. Vamvoudakis , Wassim M. Haddad

{"title":"Fixed-time learning for safe time-critical verification using reachability analysis","authors":"Nick-Marios T. Kokolakis , Kyriakos G. Vamvoudakis , Wassim M. Haddad","doi":"10.1016/j.automatica.2025.112528","DOIUrl":"10.1016/j.automatica.2025.112528","url":null,"abstract":"<div><div>In this paper, we address a safe time-critical control problem using reachability analysis and design a reinforcement learning-based mechanism for learning online and in fixed-time the solution to the safe time-critical control problem. Safety is ensured by determining a set of states for which there exists an admissible control law generating a system trajectory that does not reach a set of forbidden states at a user-prescribed time instant. Specifically, we cast our safe time-critical problem as a Mayer optimal feedback control problem whose solution satisfies the Hamilton–Jacobi–Bellman (HJB) equation and characterizes the set of safe states. Since the HJB equation is generally difficult to solve, we develop an online critic-only reinforcement learning-based algorithm for simultaneously learning the solution to the HJB equation and the safe set in a fixed time. In particular, we introduce a non-Lipschitz experience replay-based learning law utilizing recorded and current data for updating the critic weights to learn the value function and the safe set. The non-Lipschitz property of the dynamics gives rise to fixed-time convergence, whereas the experience replay-based approach eliminates the need to satisfy the persistence of excitation condition provided that a recorded data set is sufficiently rich. Simulation results illustrate the efficacy of the proposed approach to the problem of fixed-wing unmanned aerial vehicle collision avoidance.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"183 ","pages":"Article 112528"},"PeriodicalIF":5.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926769","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

Maximum principle for partial information non-zero sum stochastic differential games with mixed delays 混合时滞的部分信息非零和随机微分对策的极大原理

IF 5.9 2区计算机科学

Automatica Pub Date : 2025-09-01 DOI: 10.1016/j.automatica.2025.112570

Pan Chen , Feng Zhang

引用次数: 0

A new input design method for online active fault diagnosis based on sum-of-ratios optimization 基于比率和优化的在线主动故障诊断输入设计新方法

IF 5.9 2区计算机科学

Automatica Pub Date : 2025-08-29 DOI: 10.1016/j.automatica.2025.112559

Bo Min , Feng Xu , James Lam , Chenchen Fan , Lin Lin

{"title":"A new input design method for online active fault diagnosis based on sum-of-ratios optimization","authors":"Bo Min , Feng Xu , James Lam , Chenchen Fan , Lin Lin","doi":"10.1016/j.automatica.2025.112559","DOIUrl":"10.1016/j.automatica.2025.112559","url":null,"abstract":"<div><div>This paper proposes a new input design method for set-based active fault diagnosis (AFD) of discrete-time linear time-invariant (LTI) systems. The principle of set separation tendency-based AFD methods focuses on designing inputs to increase the separation tendency of output sets associated with different system modes and to discriminate inconsistent system modes step by step. Compared to existing works that aim to enlarge the separation tendency of output zonotopes, a new and enhanced characterization of the dispersity of a group of output zonotopes is introduced. Specifically, this dispersity is defined as the sum of ratios of the center distance to the total size of two different zonotopes in each pair, effectively describing the separation tendency of a group of output zonotopes. The objective of enlarging the separation tendency of output zonotopes is to maximize the newly introduced dispersity of output zonotopes at each time instant, achieved by formulating the design of one-step input as a sum-of-ratios problem. After a series of transformations, this problem is effectively solved using the 0<span><math><mo>−</mo></math></span>1 mixed integer quadratic programming framework. At the end of this paper, a numerical example is provided to illustrate the effectiveness of the proposed method.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"182 ","pages":"Article 112559"},"PeriodicalIF":5.9,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913879","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 adaptive safe-oriented barrier Lyapunov function-based boundary control of flexible beam systems characterized by uncertain Euler–Bernoulli PDEs 不确定Euler-Bernoulli偏微分方程柔性梁系统的事件触发自适应安全导向障碍Lyapunov函数边界控制

IF 5.9 2区计算机科学

Automatica Pub Date : 2025-08-28 DOI: 10.1016/j.automatica.2025.112555

Libei Sun , Yongduan Song , Maolong Lv , Xiucai Huang , Changyun Wen

{"title":"Event-triggered adaptive safe-oriented barrier Lyapunov function-based boundary control of flexible beam systems characterized by uncertain Euler–Bernoulli PDEs","authors":"Libei Sun , Yongduan Song , Maolong Lv , Xiucai Huang , Changyun Wen","doi":"10.1016/j.automatica.2025.112555","DOIUrl":"10.1016/j.automatica.2025.112555","url":null,"abstract":"<div><div>The territory of safe boundary control for PDE characterized flexible beam systems in an event-triggering context is both intriguing and under-explored. The underlying problem becomes even more complicated if such systems are subject to potentially conflicting time-varying hard and soft constraints, as well as uncertainties and disturbances. In this study, we present a solution to this technically significant and challenging problem. Firstly, we introduce a dynamic constraint region with an adjustable planning scheme, facilitating the establishment of time-varying constraints and prescribed soft constraint recovery. Within this strategy, higher priority is given to hard constraints, ensuring that safety requirements are consistently met, while soft constraints are accommodated only when they align with the hard constraints. Secondly, we develop an event-triggered adaptive safe boundary controller, where the actuator signal and parameter estimators are executed intermittently on an event-driven basis, ensuring that both the control input and parameter estimates employ piecewise-constant values. Consequently, the unknown damping coefficients (i.e., viscous, structural, and Kelvin–Voigt damping), the bending stiffness, and boundary disturbances are handled simultaneously, while effectively suppressing undesired vibrations or even resonances in the control input posed by the transient of adaptive learning. Through co-design, we guarantee the safety and stability of the closed-loop system, ensuring a minimal dwell-time between triggering instants, as rigorously verified by Lyapunov analysis. Finally, we validate the benefits and efficiency of the proposed algorithm through comprehensive numerical simulations.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"182 ","pages":"Article 112555"},"PeriodicalIF":5.9,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913877","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