Automatica最新文献_第7页

Parametrization approach for real-time generation of minimum-effort trajectories via neural network 基于神经网络的最小努力轨迹实时生成的参数化方法

IF 5.9 2区计算机科学

Automatica Pub Date : 2026-03-01 Epub Date: 2026-01-02 DOI: 10.1016/j.automatica.2025.112817

Han Wang, Zheng Chen

{"title":"Parametrization approach for real-time generation of minimum-effort trajectories via neural network","authors":"Han Wang, Zheng Chen","doi":"10.1016/j.automatica.2025.112817","DOIUrl":"10.1016/j.automatica.2025.112817","url":null,"abstract":"<div><div>This paper is concerned with real-time generation of optimal flight trajectories for Minimum-Effort Control Problems (MECPs), which is fundamentally important for autonomous flight of aerospace vehicles. Although existing optimal control methods, such as indirect methods and direct methods, can be amended to solve MECPs, it is very challenging to obtain, in real time, the solution trajectories since those methods suffer the issue of convergence. As the artificial neural network can generate its output within a constant time, it has been alternative for real-time generation of optimal trajectories in the literature. The usual way is to train neural networks by solutions from indirect or direct methods, which, however, cannot ensure sufficient conditions for local optimality to be met. As a result, the trained neural networks cannot be guaranteed to generate at least locally optimal trajectories. To address this issue, a parametrization approach is developed in the paper so that not only necessary but also sufficient conditions for local optimality are embedded into a parameterized set of differential equations. This allows generating the dataset of at least locally optimal trajectories through solving some initial value problems. Once a neural network is trained by the dataset constructed by the parametrization approach, it not only can generate optimal trajectories within milliseconds but also ensure the generated trajectories to be at least locally optimal, as finally demonstrated by two conventional MECPs in aerospace engineering.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112817"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884871","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 optimal control for linear quadratic tracking with unknown target states 未知目标状态线性二次跟踪的逆最优控制

IF 5.9 2区计算机科学

Automatica Pub Date : 2026-03-01 Epub Date: 2026-01-07 DOI: 10.1016/j.automatica.2026.112819

Yao Li , Chengpu Yu , Hao Fang , Jie Chen

{"title":"Inverse optimal control for linear quadratic tracking with unknown target states","authors":"Yao Li , Chengpu Yu , Hao Fang , Jie Chen","doi":"10.1016/j.automatica.2026.112819","DOIUrl":"10.1016/j.automatica.2026.112819","url":null,"abstract":"<div><div>This paper addresses the inverse optimal control for the linear quadratic tracking problem with a fixed but unknown target state, which aims to estimate the possible triplets comprising the target state, the state weight matrix, and the input weight matrix from observed optimal control input and the corresponding state trajectories. Sufficient conditions have been provided for the unique determination of both the linear quadratic cost function as well as the target state. A computationally efficient and numerically reliable parameter identification algorithm is proposed by equating optimal control strategies with a system of linear equations, and the associated relative error upper bound is derived in terms of data volume and signal-to-noise ratio (SNR). Moreover, the proposed inverse optimal control algorithm is applied for the joint cluster coordination and intent identification of a multi-agent system. By incorporating the structural constraint of the Laplace matrix, the relative error upper bound can be reduced accordingly. Finally, the algorithm’s efficiency and accuracy are validated by a vehicle-on-a-lever example and a multi-agent formation control example.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112819"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939142","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

Exact solution to the least squares realization problem as a multiparameter eigenvalue problem 将最小二乘实现问题精确解为多参数特征值问题

IF 5.9 2区计算机科学

Automatica Pub Date : 2026-03-01 Epub Date: 2025-12-20 DOI: 10.1016/j.automatica.2025.112792

Sibren Lagauw, Lukas Vanpoucke, Bart De Moor

引用次数: 0

Data-enabled iterative learning control: A zero-sum game design for time-scale-varying tasks 数据支持迭代学习控制：时间尺度变化任务的零和游戏设计

IF 5.9 2区计算机科学

Automatica Pub Date : 2026-03-01 Epub Date: 2025-12-20 DOI: 10.1016/j.automatica.2025.112781

Zhihe Zhuang , Rodrigo A. González , Hongfeng Tao , Wojciech Paszke , Tom Oomen

{"title":"Data-enabled iterative learning control: A zero-sum game design for time-scale-varying tasks","authors":"Zhihe Zhuang , Rodrigo A. González , Hongfeng Tao , Wojciech Paszke , Tom Oomen","doi":"10.1016/j.automatica.2025.112781","DOIUrl":"10.1016/j.automatica.2025.112781","url":null,"abstract":"<div><div>Iterative learning control (ILC) is an intelligent control methodology for tackling iteration-invariant exogenous inputs. It is of great significance to develop its extrapolation for more general repetitive tasks with mutual similarity, e.g., tasks with different time scales. In practice, discrete-time ILC with sampling behavior for time-scale-varying tasks suffers from the failure of perfect corresponding learning and environment-dependent iteration-varying disturbances. This paper develops a novel direct data-based ILC algorithm using off-policy Q-learning for tasks with varying time scales, enabling the robust learning of an optimal ILC policy from experimental input/output (I/O) data. From a two-player zero-sum game perspective, the iteration-varying disturbance generated from the varying time scales of repetitive tasks is tackled quantitatively with a preset disturbance attenuation level. Further, to emphasize the importance of theoretical guarantees of reinforcement learning (RL)-based ILC designs, the data efficiency of the developed algorithm is enhanced based on Willems’ Fundamental Lemma, and a rigorous convergence analysis is given. The simulation model of an F-16 aircraft autopilot is employed to show the effectiveness of the developed approach.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112781"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784438","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 multi-objective hierarchical control framework for battery units in DC microgrids using improved adaptive droop control 基于改进自适应下垂控制的直流微电网电池单元多目标分层控制框架

IF 5.9 2区计算机科学

Automatica Pub Date : 2026-03-01 Epub Date: 2025-12-22 DOI: 10.1016/j.automatica.2025.112762

Yangyang Qian , Zongli Lin , Yacov A. Shamash

{"title":"A multi-objective hierarchical control framework for battery units in DC microgrids using improved adaptive droop control","authors":"Yangyang Qian , Zongli Lin , Yacov A. Shamash","doi":"10.1016/j.automatica.2025.112762","DOIUrl":"10.1016/j.automatica.2025.112762","url":null,"abstract":"<div><div>We investigate the primary/secondary control problem for battery units with adaptive droop control in DC microgrids. A majority of existing control methods for state-of-charge (SoC) balancing among battery units require that the terminal voltage of each battery unit remains constant. To remove such a requirement, at the primary control level, we propose an improved adaptive droop control method by introducing an auxiliary battery state in terms of the battery parameters, the terminal voltage, and a power function of the SoC. The designed droop coefficient is inversely proportional to the auxiliary battery state. It is shown that the proposed adaptive droop control achieves proportional power sharing and SoC balancing, albeit at a cost of compromised voltage regulation. A salient feature of the proposed adaptive droop control method is that the battery’s terminal voltage is allowed to be time-varying. Moreover, to compensate for the voltage deviation caused by the improved adaptive droop control, we propose a consensus-based distributed control scheme at the secondary control level. It is shown that the resulting hierarchical control framework, consisting of the primary and secondary control levels, guarantees that the control objectives of voltage regulation, proportional power sharing, and SoC balancing are all achieved. Simulation studies based on an accurate electrical battery model validate the effectiveness of our multi-objective hierarchical control framework.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112762"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841798","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 algorithms for solving linear algebraic equations: An optimal control perspective 求解线性代数方程的分布式算法：最优控制视角

IF 5.9 2区计算机科学

Automatica Pub Date : 2026-03-01 Epub Date: 2025-12-19 DOI: 10.1016/j.automatica.2025.112772

Juntao Li , Cong Liang , Deyuan Meng

引用次数: 0

Corrigendum to ‘On the Detection of Markov Decision Processes’ [Automatica 175 (2025) 112196] “关于马尔可夫决策过程的检测”的更正[Automatica 175 (2025) 112196]

IF 5.9 2区计算机科学

Automatica Pub Date : 2026-03-01 Epub Date: 2025-12-15 DOI: 10.1016/j.automatica.2025.112725

Xiaoming Duan , Yagiz Savas , Rui Yan , Zhe Xu , Ufuk Topcu

引用次数: 0

SafEDMD: A Koopman-based data-driven controller design framework for nonlinear dynamical systems SafEDMD：一种基于koopman的非线性动力系统数据驱动控制器设计框架

IF 5.9 2区计算机科学

Automatica Pub Date : 2026-03-01 Epub Date: 2025-12-17 DOI: 10.1016/j.automatica.2025.112732

Robin Strässer , Manuel Schaller , Karl Worthmann , Julian Berberich , Frank Allgöwer

引用次数: 0

Inverse optimal design of input-to-state stabilizing homogeneous controllers for nonlinear homogeneous systems 非线性齐次系统输入状态稳定齐次控制器的逆优化设计

IF 5.9 2区计算机科学

Automatica Pub Date : 2026-03-01 Epub Date: 2025-12-18 DOI: 10.1016/j.automatica.2025.112770

Kaixin Lu , Ziliang Lyu , Haoyong Yu

引用次数: 0

Event-triggered privacy-preserving consensus of uncertain nonlinear systems: A full output mask approach 事件触发的不确定非线性系统的隐私保护共识：一种全输出掩码方法

IF 5.9 2区计算机科学

Automatica Pub Date : 2026-03-01 Epub Date: 2025-12-18 DOI: 10.1016/j.automatica.2025.112793

Rui Gao , Jiangshuai Huang , Changyun Wen

{"title":"Event-triggered privacy-preserving consensus of uncertain nonlinear systems: A full output mask approach","authors":"Rui Gao , Jiangshuai Huang , Changyun Wen","doi":"10.1016/j.automatica.2025.112793","DOIUrl":"10.1016/j.automatica.2025.112793","url":null,"abstract":"<div><div>Achieving distributed task execution with privacy preservation in multi-agent dynamical systems under eavesdropping attacks remains a challenging problem. Most existing schemes focus on eavesdroppers between adjacent agents, while state information leakage from controller or parameter estimator channels via smart eavesdroppers is rarely considered. Moreover, uncertainty of nonlinear multi-agent systems cannot be addressed by approaches developed for linear systems. This paper proposes a control framework that combines a full output mask method with an event-triggered mechanism. Output mask functions are applied to both sensor and parameter estimator channels to resist eavesdroppers on inter-agent links as well as on agents own channels. To overcome the non-differentiability of virtual controllers caused by masked outputs with unknown forms and parameters, each recursive step employs unmasked raw information. Coupling among leader-following consensus, parameter estimation, and privacy protection in uncertain nonlinear multi-agent systems is further addressed through multi-channel triggering conditions and parameter design. Theoretical analysis proves that output consensus tracking errors converge to a small set while ensuring privacy preservation, which is validated by simulation results.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112793"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799651","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