Automatica最新文献

英文中文

Safety-critical control of discontinuous systems with nonsmooth safe sets 非光滑安全集不连续系统的安全临界控制

IF 5.9 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2026-03-01 Epub Date: 2025-12-23 DOI: 10.1016/j.automatica.2025.112780

Mohammed M.J. Alyaseen, Nikolay Atanasov, Jorge Cortes

This paper studies the design of controllers for discontinuous dynamics that ensure the safety of non-smooth sets. The safe set is represented by arbitrarily nested unions and intersections of 0-superlevel sets of differentiable functions. We show that the satisfaction of the point-wise active safety constraints only does not necessarily imply safety. This rules out the standard techniques developed for safety of continuous dynamics. This motivates the introduction of the notion of transition functions, which allow us to incorporate even the inactive safety constraints without falling into unnecessary conservatism. These functions allow system trajectories to leave a component of the nonsmooth safe set to transition to a different one. The resulting controller is then defined as the solution to a convex optimization problem, which we show is feasible and continuous wherever the system dynamics is continuous. We illustrate the effectiveness of the proposed design approach in a multi-agent coverage control problem.

研究了保证非光滑集安全的不连续动力学控制器的设计。安全集由任意嵌套的可微函数的0超层集的并集和交集表示。我们证明了仅仅满足点主动安全约束并不一定意味着安全。这就排除了为连续动力学安全而开发的标准技术。这促使引入过渡函数的概念，它允许我们合并甚至不活跃的安全约束，而不会陷入不必要的保守主义。这些函数允许系统轨迹离开非光滑安全集的一个组成部分，过渡到另一个组成部分。然后将所得到的控制器定义为一个凸优化问题的解，我们证明了无论系统动力学是连续的，它都是可行的和连续的。我们举例说明了所提出的设计方法在多智能体覆盖控制问题中的有效性。

引用次数: 0

Exploring the complex relationship between virus transmission and opinion evolution: An individual-based approach 病毒传播与舆论演变之间的复杂关系：基于个体的方法

IF 5.9 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2026-03-01 Epub Date: 2025-12-16 DOI: 10.1016/j.automatica.2025.112757

Shidong Zhai , Ji Xiang Cao , Wei Zhu

The paper delves into the intricate interplay between individual opinions on viruses and the transmission of infectious diseases. A new network model is proposed, integrating epidemic dynamics with the evolution of opinions, taking into account factors such as persuasion sensitivity, competitive interactions, and public vigilance. By introducing an opinion-dependent reproduction number, a comprehensive analysis of the model’s dynamics is conducted. The paper thoroughly investigates various equilibrium states and their stability, providing valuable insights into how opinions can affect the disease transmission process and potential containment measures. Furthermore, the paper outlines strategies for epidemic mitigation, including adjusting transmission dynamics and influencing public sentiment, offering practical guidance for authorities seeking to manage outbreaks effectively. Finally, the paper presents numerical simulations and validation using real data.

这篇论文深入研究了个人对病毒的看法与传染病传播之间错综复杂的相互作用。提出了一种新的网络模型，将疫情动态与舆论演变结合起来，考虑到说服敏感性、竞争互动和公众警惕性等因素。通过引入与意见相关的再现数，对模型的动力学进行了全面分析。本文深入研究了各种平衡状态及其稳定性，为意见如何影响疾病传播过程和潜在的控制措施提供了有价值的见解。此外，该文件概述了缓解流行病的战略，包括调整传播动态和影响公众情绪，为寻求有效管理疫情的当局提供了实用指导。最后进行了数值模拟，并用实际数据进行了验证。

引用次数: 0

Systematic LMI approaches to design multivariable sliding mode controllers for uncertain systems 不确定系统多变量滑模控制器设计的系统LMI方法

IF 5.9 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2026-03-01 Epub Date: 2025-12-17 DOI: 10.1016/j.automatica.2025.112747

Pedro Henrique Silva Coutinho , Iury Bessa , Victor Hugo Pereira Rodrigues , Tiago Roux Oliveira

This paper addresses sliding mode control for uncertain multivariable systems. In this sense, we present systematic procedures to design variable structure controllers (VSCs) and unit-vector controllers (UVCs) for stabilization and tracking in finite time. Although these controller classes are known to be effective, the literature lacks systematic design methods. By using appropriate representations of the closed-loop system, we derive sufficient conditions that enable the systematic design of robust sliding mode controllers based on semidefinite programming with linear matrix inequalities (LMIs) constraints. The proposed approach ensures that the origin of the closed-loop system is finite-time stable. Additionally, since the reaching time depends on initial conditions and the decay rate, the design approach uses convex optimization problems to minimize the reaching time for a given set of initial conditions. Numerical examples demonstrate the effectiveness of the proposed techniques.

本文研究不确定多变量系统的滑模控制问题。在这个意义上，我们提出了系统的程序来设计可变结构控制器（VSCs）和单位矢量控制器（uvc）在有限时间内的稳定和跟踪。虽然已知这些控制器类是有效的，但文献中缺乏系统的设计方法。通过使用闭环系统的适当表示，我们得到了基于线性矩阵不等式约束的半定规划鲁棒滑模控制器的系统设计的充分条件。该方法保证了闭环系统的原点是有限时间稳定的。此外，由于到达时间取决于初始条件和衰减率，设计方法使用凸优化问题来最小化给定一组初始条件的到达时间。数值算例验证了所提方法的有效性。

引用次数: 0

A convex reformulation for speed planning of a vehicle under the travel time and energy consumption objectives 基于行程时间和能耗目标的车辆速度规划的凸重构

IF 5.9 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2026-03-01 Epub Date: 2025-12-29 DOI: 10.1016/j.automatica.2025.112811

Luca Consolini, Mattia Laurini, Marco Locatelli

In this paper we address the speed planning problem for a vehicle along a predefined path. A weighted sum of two conflicting objectives, energy consumption and travel time, is minimized. After deriving a non-convex mathematical model of the problem, we prove that the feasible region of this problem is a lattice. Moreover, we introduce a feasibility-based bound-tightening technique which allows us to derive the minimum and maximum element of the lattice, or establish that the feasible region is empty. We prove the exactness of a convex relaxation of the non-convex problem, obtained by replacing all constraints with the lower and upper bounds for the variables corresponding to the minimum and maximum elements of the lattice, respectively. After proving some properties of optimal solutions of the convex relaxation, we exploit them to develop a dynamic programming approach returning an approximate solution to the convex relaxation, and with time complexity

O (n^{2})

, where

n

is the number of points into which the continuous path is discretized.

本文研究了车辆沿预定路径的速度规划问题。能源消耗和旅行时间这两个相互冲突的目标的加权和被最小化。在推导了该问题的非凸数学模型后，证明了该问题的可行域是格。此外，我们引入了一种基于可行性的边界收紧技术，该技术允许我们推导晶格的最小和最大元素，或者确定可行区域是空的。我们证明了非凸问题的凸松弛的精确性，通过用格的最小和最大元素对应的变量的下界和上界分别代替所有约束而得到。在证明了凸松弛最优解的一些性质之后，我们利用它们开发了一种动态规划方法，该方法返回凸松弛的近似解，其时间复杂度为O(n2)，其中n为连续路径离散到的点数。

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引用次数: 0

Information-triggered learning with application to learning-based predictive control 信息触发学习及其在基于学习的预测控制中的应用

IF 5.9 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2026-03-01 Epub Date: 2025-12-19 DOI: 10.1016/j.automatica.2025.112746

Kaikai Zheng , Dawei Shi , Sandra Hirche , Yang Shi

Learning-based control has attracted significant attention in recent years, especially for plants that are difficult to model based on first-principles. A key issue in learning-based control is how to make efficient use of data as the abundance of data becomes overwhelming. To address this issue, this work proposes an information-triggered learning framework and a corresponding learning-based controller design approach with guaranteed stability. Specifically, we consider a linear time-invariant system with unknown dynamics. A set-membership approach is introduced to learn a parametric uncertainty set for the unknown dynamics. Then, a data selection mechanism is proposed by evaluating the incremental information in a data sample, where the incremental information is quantified by its effects on shrinking the parametric uncertainty set. Next, after introducing a stability criterion using the set-membership estimate of the system dynamics, a robust learning-based predictive controller (LPC) is designed by minimizing a worst-case cost function. The closed-loop stability of the LPC equipped with the information-triggered learning protocol is discussed within a high-probability framework. Finally, comparative numerical experiments are performed to verify the validity of the proposed approach.

近年来，基于学习的控制引起了人们的极大关注，特别是对于难以基于第一性原理建模的植物。基于学习的控制的一个关键问题是如何有效地利用数据，因为大量的数据变得势不可挡。为了解决这个问题，本工作提出了一个信息触发的学习框架和相应的基于学习的控制器设计方法，并保证了稳定性。具体来说，我们考虑一个具有未知动力学的线性定常系统。引入集隶属度法学习未知动力学的参数不确定性集。然后，通过评估数据样本中的增量信息，提出了一种数据选择机制，其中增量信息通过其对参数不确定性集的收缩效果来量化。其次，利用系统动力学的集隶属度估计引入稳定性准则后，通过最小化最坏情况代价函数设计了基于学习的鲁棒预测控制器（LPC）。在高概率框架下讨论了配备信息触发学习协议的LPC的闭环稳定性。最后，通过数值对比实验验证了所提方法的有效性。

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引用次数: 0

Strongly stabilizing the Acrobot by optimizing maximal relative stability via observation point selection 通过观察点选择优化最大相对稳定性，使Acrobot具有较强的稳定性

IF 5.9 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2026-03-01 Epub Date: 2025-12-30 DOI: 10.1016/j.automatica.2025.112814

Xin Xin , Xiaohui Ji , Ziyu Wang , Yannian Liu

In this paper, we investigate strong stabilization for the Acrobot by optimizing its maximal relative stability through selecting an observation point on its second link. Prior work designed a second-order, strongly stabilizing controller that achieves maximal relative stability (the minimum distance from the closed-loop poles to the imaginary axis) for the plant obtained via a fixed observation point, whose transfer function has a nonnegative real zero. We first determine the value of this nonnegative real zero that maximizes maximal relative stability. We prove that maximal relative stability increases monotonically as this real zero moves toward the origin and attains the maximum when the plant has a double zero at the origin. We then extend the existing controller design to plants with conjugate imaginary-axis zeros, proving that maximal relative stability increases monotonically as these zeros move away from the origin within our identified interval. We also prove that all resulting stable second-order controllers are minimum phase.

本文通过在机器人的第二连杆上选择一个观测点来优化机器人的最大相对稳定性，从而研究了机器人的强稳定性。先前的工作设计了一个二阶强稳定控制器，该控制器通过一个固定的观测点获得被控对象的最大相对稳定性（从闭环极点到虚轴的最小距离），该观测点的传递函数为非负实零。我们首先确定使最大相对稳定性最大化的非负实零的值。我们证明了最大相对稳定性随着这个实零向原点移动而单调增加，当目标在原点处有一个双零时达到最大值。然后，我们将现有的控制器设计扩展到具有共轭虚轴零的对象，证明当这些零在我们确定的区间内远离原点时，最大相对稳定性单调增加。并证明了所得到的二阶稳定控制器都是最小相位的。

引用次数: 0

Distributed Nash equilibrium computation in multi-group resource allocation games over digraphs 有向图上多群资源分配博弈的分布式纳什均衡计算

IF 5.9 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2026-03-01 Epub Date: 2026-01-02 DOI: 10.1016/j.automatica.2025.112816

Jialing Zhou , Guanghui Wen , Yuezu Lv , Xinlei Yi , Tao Yang , Karl Henrik Johansson

The existing distributed resource allocation (DRA) algorithms for multi-agent networks can rarely be implemented for multiple interacting groups of agents with conflicts of interest. The directed interaction, together with the hard balance constraint that follows from maintaining supply–demand balance during the execution process, make the DRA more challenging. To address this problem, the paper studies DRA over multiple interacting groups from a game-theoretic perspective, introducing the resource allocation game (RAG). A novel out-Laplacian matrix based methodology is developed for distributed Nash equilibrium (NE) computation. Following this methodology, distributed algorithms are designed using leader-follower tracking protocols to estimate partial derivatives of individual objective functions for the RAG. A reduced-order distributed algorithm is further developed for the RAG by integrating a gradient-tracking mechanism for estimating partial derivatives of group-level objective functions. It is shown that agent states converge to the NE of the games linearly while satisfying the balance constraint during the whole execution process under the proposed algorithms. The effectiveness of the proposed algorithms is illustrated through numerical examples.

现有的多智能体网络分布式资源分配（DRA）算法很少适用于存在利益冲突的多智能体交互组。直接的交互，以及在执行过程中维护供需平衡所带来的硬平衡约束，使DRA更具挑战性。为了解决这一问题，本文从博弈论的角度研究了多交互群体的资源分配博弈，引入了资源分配博弈（RAG）。提出了一种基于外拉普拉斯矩阵的分布式纳什均衡计算方法。按照这种方法，分布式算法被设计为使用领导者-追随者跟踪协议来估计RAG的单个目标函数的偏导数。通过集成梯度跟踪机制，进一步开发了一种用于估计群级目标函数偏导数的低阶分布算法。结果表明，在该算法的整个执行过程中，智能体状态在满足平衡约束的情况下线性收敛于博弈的NE。通过数值算例说明了所提算法的有效性。

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引用次数: 0

Nonlinear bidirectional dynamic latent modeling for closed-loop process monitoring 闭环过程监测的非线性双向动态潜建模

IF 5.9 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2026-03-01 Epub Date: 2025-12-30 DOI: 10.1016/j.automatica.2025.112751

Xu Chen , Xiao He , S. Joe Qin

Closed-loop control brings new challenges for process monitoring. Nonlinearities in the correlation structure and the dynamic relations in closed-loop process data further complicate the task. To tackle these issues, a new dynamic latent variable scheme is proposed in this paper for nonlinear closed-loop processes. The nonlinearities of the processes are investigated in detail first, followed by a dynamic latent variable object for latent structure extraction of nonlinear closed-loop processes. An algorithm is then proposed to solve the constructed optimization object which has guaranteed convergency. The relations of the extracted dynamic latent model are revealed in detail afterwards, and the process monitoring strategy is built with comprehensive analysis of variations in closed-loop process data. A numerical simulation and a study on the thruster system of the “Jiaolong” deep-sea submarine are carried on to show the performance of the proposed scheme.

闭环控制给过程监控带来了新的挑战。相关结构的非线性和闭环过程数据的动态关系进一步使任务复杂化。为了解决这些问题，本文提出了一种新的非线性闭环过程动态隐变量格式。首先对过程的非线性进行了详细的研究，然后建立了一个动态潜变量目标，用于非线性闭环过程的潜结构提取。然后提出了一种保证收敛性的算法来求解所构造的优化对象。随后详细揭示了提取的动态潜模型之间的关系，并通过对闭环过程数据变化的综合分析，建立了过程监控策略。通过对“蛟龙”号潜艇推进器系统的数值仿真和研究，验证了该方案的有效性。

引用次数: 0

Constrained output regulation for linear systems performing iterative tasks via robust learning MPC 基于鲁棒学习MPC的线性系统迭代任务约束输出调节

IF 5.9 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2026-03-01 Epub Date: 2025-12-29 DOI: 10.1016/j.automatica.2025.112801

Yu Xiao , Yuan Yuan , Biao Huang , Xiaodong Xu

The constrained output regulation problem has been addressed by the output feedback model predictive control (MPC) algorithm for deterministic system without random disturbances in recent years. This paper further considers the case with the presence of unknown-but-bounded random disturbances in the linear systems performing iterative regulation tasks, which have not been considered. When pursuing infinite-horizon optimization, unknown random disturbances give rise to an infinite horizon policy optimization problem in the MPC controller, and the problem cannot be directly solved. Moreover, the recursive feasibility in dealing with iterative tasks for the deterministic system cannot be guaranteed for uncertain systems, which renders the solution of this paper nontrivial. To this end, we develop a novel robust learning-based MPC algorithm such that the problem of linear iterative output regulation in the presence of unknown-but-bounded random disturbances along with state and input constraints is addressed.

近年来，输出反馈模型预测控制（MPC）算法解决了无随机干扰确定性系统的约束输出调节问题。本文进一步考虑了在执行迭代调节任务的线性系统中存在未知但有界的随机干扰的情况，这是以前没有考虑过的。在进行无穷视界优化时，未知随机干扰会在MPC控制器中产生一个无穷视界策略优化问题，该问题无法直接求解。此外，对于不确定系统，不能保证确定系统处理迭代任务的递归可行性，这使得本文的解具有非平凡性。为此，我们开发了一种新的鲁棒的基于学习的MPC算法，从而解决了在存在未知但有界的随机干扰以及状态和输入约束的情况下线性迭代输出调节的问题。

引用次数: 0

Maximum-system-reliability control allocation for spacecraft with redundant actuators 具有冗余作动器的航天器最大系统可靠性控制分配

IF 5.9 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2026-03-01 Epub Date: 2025-12-19 DOI: 10.1016/j.automatica.2025.112779

Jianchun Zhang , Xiang Yu , Jianzhong Qiao , Lei Guo

On-orbit useful lifetime is a critical metric for spacecraft missions, for which we propose a maximum-system-reliability control allocation scheme for spacecraft with redundant actuators. The dynamic reliability model for the redundant actuator system is formulated, incorporating an online parameter estimation method initialized with offline estimates. On this basis, we define a control allocation optimization problem that maximizes the one-step system reliability prediction. To enable efficient online computation, a recursive optimization algorithm is introduced. Theoretical analysis proves that the proposed approach can extend the useful lifetime of the redundant actuator system with any failure mode. A comparative example of spacecraft attitude stabilization validates the feasibility, generality, and superiority of the proposed method.

在轨使用寿命是航天器任务的一个关键指标，针对这一指标，提出了一种具有冗余作动器的航天器最大系统可靠性控制分配方案。建立了冗余作动器系统的动态可靠性模型，采用离线估计初始化的在线参数估计方法。在此基础上，我们定义了一个使一步系统可靠性预测最大化的控制分配优化问题。为了实现高效的在线计算，引入了递归优化算法。理论分析表明，该方法可以延长任意失效模式下冗余作动器系统的使用寿命。以航天器姿态稳定为例，验证了该方法的可行性、通用性和优越性。

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全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

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