ISA transactions最新文献

英文中文

Zero-sum game-based dynamic self-triggered sliding mode control for unknown nonlinear systems with asymmetric input constraints 输入约束不对称的未知非线性系统的零和博弈动态自触发滑模控制。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2026-01-01 DOI: 10.1016/j.isatra.2025.11.014

Ning Xu , Tengda Wang , Ben Niu , Guangdeng Zong , Xudong Zhao , Guangjing Song

This article addresses a zero-sum game-based dynamic self-triggered sliding mode control problem for continuous-time unknown nonlinear systems with asymmetric input constraints using the adaptive dynamic programming method. First, a novel nonquadratic value function incorporating sliding mode variables is formulated to systematically convert the

H_{\infty}

constrained control problem into an unconstrained zero-sum game problem. A data-driven method employing recurrent neural networks subsequently reconstructs the unknown system dynamics. Building upon this foundation, a novel dynamic self-triggered mechanism is designed, where triggering instants are adaptively determined through predictive computation involving current state information and time-decaying dynamic thresholds. To resolve the derived Hamilton-Jacobi-Isaacs equation, a streamlined single-critic neural architecture is proposed, eliminating the conventional actor-network dependency while preserving approximation accuracy. Through rigorous Lyapunov-based analysis, all closed-loop signals are theoretically guaranteed to achieve uniform ultimate boundedness. The designed control strategy is conclusively validated through comprehensive simulation studies implemented on a robotic arm system and a microgrid system, demonstrating excellent dynamic response characteristics.

本文采用自适应动态规划方法研究了输入约束不对称的连续未知非线性系统基于零和博弈的动态自触发滑模控制问题。首先，构造了一种新的包含滑模变量的非二次值函数，将H∞约束控制问题系统地转化为无约束零和博弈问题。采用递归神经网络的数据驱动方法重建未知的系统动力学。在此基础上，设计了一种新的动态自触发机制，该机制通过涉及当前状态信息和时间衰减动态阈值的预测计算自适应确定触发时刻。为了求解导出的Hamilton-Jacobi-Isaacs方程，提出了一种流线型的单批评家神经结构，消除了传统的行动者-网络依赖，同时保持了近似精度。通过严格的李雅普诺夫分析，从理论上保证了所有闭环信号达到一致的最终有界性。通过对机械臂系统和微电网系统的综合仿真研究，最终验证了所设计的控制策略，显示出良好的动态响应特性。

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引用次数: 0

Event-triggered adaptive iterative learning prescribed performance control of high-order strict feedback nonlinear systems 高阶严格反馈非线性系统的事件触发自适应迭代学习规定性能控制。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2026-01-01 DOI: 10.1016/j.isatra.2025.11.028

Leyan Fang, Mingzhe Hou, Xindi Xu, Guangren Duan

Most adaptive iterative learning control techniques involving variable constraints are primarily based on first-order system structures and neglect the impact of limited network communication resources. To address these limitations, this paper investigates the event-triggered adaptive iterative learning prescribed performance control (PPC) approach for a class of uncertain high-order strict feedback nonlinear systems. The considered systems contain linearly parameterized uncertainties in both the drift terms and the control coefficients. During each iteration, a preset error trajectory is constructed within the performance envelope to eliminate the limitation of initial conditions and the error of the actual tracking error relative to the preset one is kept small enough to realize the PPC. The detailed event-triggered adaptive iterative learning PPC algorithm is designed by treating every high-order subsystem as a whole and comprehensively utilizing the event-triggering mechanism, the nonlinear mapping technology and the dynamic surface control method. Moreover, an improved parameter estimation error reconstruction mechanism is given to modify the differential-difference adaptive laws and thus strengthen the parameter estimation performance, meanwhile the projection operator is employed to avoid the singularity problem of the control law. It is proved that the tracking error and

L_{2}

norms of the parameter estimation errors are uniformly ultimately bounded along the iteration-axis under the interval excitation condition and the tracking error satisfies the prescribed transient performance in each iteration. The obtained results are validated via simulations on a single-link flexible-joint robot.

大多数涉及变量约束的自适应迭代学习控制技术主要基于一阶系统结构，忽略了有限网络通信资源的影响。为了解决这些局限性，本文研究了一类不确定高阶严格反馈非线性系统的事件触发自适应迭代学习规定性能控制（PPC）方法。所考虑的系统在漂移项和控制系数中都包含线性参数化的不确定性。在每次迭代过程中，在性能包络内构造一个预设的误差轨迹，消除初始条件的限制，使实际跟踪误差相对于预设跟踪误差的误差足够小，从而实现PPC。将各高阶子系统作为一个整体，综合利用事件触发机制、非线性映射技术和动态面控制方法，设计了详细的事件触发自适应迭代学习PPC算法。提出了一种改进的参数估计误差重构机制，修正了微分-差分自适应律，提高了参数估计性能，同时采用投影算子避免了控制律的奇异性问题。证明了在区间激励条件下，参数估计误差的跟踪误差和L2范数最终沿迭代轴一致有界，且每次迭代的跟踪误差满足规定的暂态性能。通过对单连杆柔性关节机器人的仿真验证了所得结果。

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引用次数: 0

Sliding mode fault-tolerant control for manipulator based on predefined time extended state observers 基于时间扩展状态观测器的机械臂滑模容错控制。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2026-01-01 DOI: 10.1016/j.isatra.2025.11.044

Chunwu Yin, Zilan Zhao, Pei Yi

Regarding the accurate and fast-tracking control problem of a multi-joint manipulator system under the multiple uncertainties of actuator faults, parameter perturbations, and external disturbances, this paper proposes fault-tolerant control scheme based on a predefined time Extended state observer. First, a novel extended state observer has been designed to estimate the time-varying composite disturbance within a predefined time. Second, a new predefined time integral sliding mode surface and Finite time prescribed performance constraint function are constructed, and a continuous function is introduced to reduce chattering. The Lyapunov stability theory is used to analyze the predefined time stability characteristics of the closed-loop system. Finally, numerical simulations verify the effectiveness of the proposed control method, and the system has strong robustness and high steady-state accuracy, and can withstand actuator failures and other uncertain influencing factors.

针对多关节机械臂系统在执行器故障、参数扰动和外部扰动等多重不确定性下的精确快速跟踪控制问题，提出了基于预定义时间扩展状态观测器的容错控制方案。首先，设计了一种新的扩展状态观测器，在预定义时间内估计时变复合扰动。其次，构造了一个新的预定义时间积分滑模曲面和有限时间规定的性能约束函数，并引入连续函数来减小抖振；利用李雅普诺夫稳定性理论分析了闭环系统的时间稳定性特性。最后，通过数值仿真验证了所提控制方法的有效性，系统具有较强的鲁棒性和较高的稳态精度，能够承受执行器故障等不确定因素的影响。

引用次数: 0

Model-free robust tracking control of LTI systems with Markov jump target: A data informativity approach 具有马尔可夫跳跃目标的LTI系统的无模型鲁棒跟踪控制：一种数据信息性方法。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2026-01-01 DOI: 10.1016/j.isatra.2025.11.006

Zhipeng Wang , Shi Li , Shicheng Huo , Weixing Qian

In this paper, the data-driven robust output tracking problem for unknown linear time-invariant system is studied where the reference signal is generated by Markov jump systems. Unlike constructing controllers for known systems, this paper, based on data informativity, aims to develop common controllers for a family of systems corresponding to the observed data, thereby circumventing the need for persistent excitation conditions required for identifying specific dynamics. The core mission of this study is, based on observed data, to delve into the design of the sufficient and/or necessary conditions for achieving transmission zeros, controllability, and stochastic stability. Furthermore, this paper integrates these conditions through comprehensive analysis to derive the data informativity conditions that achieve robust output tracking. According to the stochastic stability criterion in the form of the data-based linear matrix inequality, the controller gains are derived. Ultimately, a mathematical model of the unmanned vehicle is established and some simulation experiments are conducted, and the effectiveness and robustness of the proposed control strategy are verified.

研究了参考信号由马尔可夫跳变系统产生的未知线性定常系统的数据驱动鲁棒输出跟踪问题。与为已知系统构建控制器不同，本文基于数据信息性，旨在为与观测数据相对应的一系列系统开发通用控制器，从而避免了识别特定动力学所需的持续激励条件的需要。本研究的核心任务是根据观测数据，深入研究实现传输零、可控性和随机稳定性的充分和/或必要条件的设计。进一步，通过综合分析，将这些条件整合在一起，推导出实现鲁棒输出跟踪的数据信息性条件。根据基于数据的线性矩阵不等式形式的随机稳定性判据，导出了控制器增益。最后，建立了无人车的数学模型，并进行了仿真实验，验证了所提控制策略的有效性和鲁棒性。

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引用次数: 0

Event-triggered adaptive fuzzy trajectory tracking control of unmanned surface vehicle with time-varying output constraints and input quantization 具有时变输出约束和输入量化的事件触发自适应模糊轨迹跟踪控制。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2026-01-01 DOI: 10.1016/j.isatra.2025.11.030

Liangtao Jin , Jun Ning , Tieshan Li

This paper proposes a novel event-triggered adaptive fuzzy control approach to tackle the trajectory tracking control problem of an unmanned surface vehicle (USV) under time-varying output constraints and input quantization effects. To address the challenges posed by model uncertainties, a fuzzy logic system (FLS) is employed for effective function approximation. An asymmetric Barrier Lyapunov Function (BLF) is incorporated into the controller to rigorously guarantee compliance with output constraints. Meanwhile, an event-triggered mechanism is developed to reduce communication overhead by avoiding unnecessary transmissions of control commands. To further reduce communication overhead, a uniform quantizer is adopted to manage input quantization, and a linear analytical model is introduced to characterize the input quantization process, thus facilitating controller design without requiring prior knowledge of quantizer parameters. The feasibility and effectiveness of the proposed control approach are demonstrated through rigorous stability analysis and comprehensive simulation studies, ensuring that the desired tracking accuracy is achieved.

针对时变输出约束和输入量化效应下的无人水面飞行器轨迹跟踪控制问题，提出了一种事件触发自适应模糊控制方法。为了解决模型不确定性带来的挑战，采用模糊逻辑系统（FLS）进行有效函数逼近。在控制器中加入了非对称势垒李雅普诺夫函数（BLF），严格保证了输出约束的遵从性。同时，提出了一种事件触发机制，通过避免不必要的控制命令传输来减少通信开销。为了进一步降低通信开销，采用均匀量化器来管理输入量化，并引入线性分析模型来表征输入量化过程，从而便于控制器的设计，而不需要事先知道量化器的参数。通过严格的稳定性分析和全面的仿真研究，证明了所提出控制方法的可行性和有效性，确保了所期望的跟踪精度。

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引用次数: 0

Interpretable fault diagnosis for variable-speed machinery via adaptive ridge extraction and causal learning 基于自适应脊提取和因果学习的变速机械可解释故障诊断。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2026-01-01 DOI: 10.1016/j.isatra.2025.11.032

Xuanshi Chen, Manyi Wang, Meijun Wang, Yilin Jiang

In most industrial processes, rotating machinery often operates under time-varying speed conditions, making it difficult to extract fault-related features using conventional diagnostic techniques. To address this challenge, a novel diagnostic framework is developed that integrates high-resolution time-frequency analysis with interpretable causal learning. First, a synchrosqueezed continuous wavelet transform (SSQ-CWT) is employed to generate a clean time-frequency representation, upon which a specialized Adaptive Rotational Frequency Ridge Extraction (ARFRE) algorithm operates to accurately estimate the instantaneous speed without a tachometer. Subsequently, features extracted from the resulting order spectrum are screened by a Causal Forest to identify a subset with true causal impact on the fault state. Finally, an interpretable decision tree is constructed using this causally validated feature set. The framework was comprehensively validated on two public benchmarks (SQV, Ottawa) and a proprietary industrial dataset. The results demonstrate superior diagnostic performance, achieving accuracies of 98.49 %, 100.00 %, and 97.44 % respectively, and consistently outperforming baseline methods. This work presents a robust, interpretable, and tachometer-free solution for fault diagnosis in variable-speed machinery, with significant potential for practical industrial application.

在大多数工业过程中，旋转机械经常在时变转速条件下运行，这使得使用传统的诊断技术很难提取与故障相关的特征。为了应对这一挑战，开发了一种新的诊断框架，该框架将高分辨率时频分析与可解释的因果学习相结合。首先，采用同步压缩连续小波变换（SSQ-CWT）生成清晰的时频表示，在此基础上，采用专门的自适应旋转频率脊提取（ARFRE）算法在不使用转速计的情况下准确估计瞬时速度。随后，从结果阶谱中提取的特征通过因果森林进行筛选，以识别对故障状态具有真正因果影响的子集。最后，利用这个因果验证的特征集构造了一个可解释的决策树。该框架在两个公共基准（SQV，渥太华）和一个专有的工业数据集上进行了全面验证。结果显示出优越的诊断性能，分别达到98.49 %，100.00 %和97.44 %的准确率，并始终优于基线方法。这项工作提出了一个强大的、可解释的、无转速表的解决方案，用于变速机械的故障诊断，具有实际工业应用的巨大潜力。

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引用次数: 0

Global fixed-time prescribed performance control with fixed-time distributed state observer for non-cooperative target tracking using multiple unmanned surface vehicles 基于固定时间分布式状态观测器的多水面无人飞行器非合作目标跟踪全局固定时间预定性能控制。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2026-01-01 DOI: 10.1016/j.isatra.2025.11.033

Haiyan Tong, Yingying Hu

This study addresses the cooperative tracking control problem for multiple unmanned surface vehicles (USVs) tasked with pursuing non-cooperative target in complex marine environments. Initially, a novel fixed-time distributed state observer (FDSO), independent of the system’s initial conditions, is developed to estimate state information of the non-cooperative target. Subsequently, a global fixed-time prescribed performance control strategy is proposed by introducing a novel exponential fixed-time shift function (EFSF), effectively eliminating traditional restrictions on error initial conditions inherent. Furthermore, a fixed-time dynamic compensation (FDC) mechanism is originally formulated to mitigate the impact of external disturbances on system performance. Through rigorous Lyapunov stability analysis, the practically fixed-time stability of the closed-loop system is theoretically proven, with theoretical guarantees derived for tracking error convergence to a predefined compact set within fixed time intervals. Extensive simulation results validate the proposed approach in terms of convergence speed, tracking accuracy, and robustness to disturbances.

研究了复杂海洋环境下多水面无人潜航器（usv）非合作目标的协同跟踪控制问题。首先，提出了一种独立于系统初始条件的固定时间分布式状态观测器（FDSO）来估计非合作目标的状态信息。随后，通过引入指数定时移位函数（EFSF），提出了一种全局定时预定性能控制策略，有效地消除了传统的误差初始条件限制。此外，还提出了一种固定时间动态补偿机制，以减轻外部干扰对系统性能的影响。通过严格的Lyapunov稳定性分析，从理论上证明了闭环系统的实际定时稳定性，并给出了跟踪误差在固定时间间隔内收敛到预定紧集的理论保证。大量的仿真结果验证了该方法在收敛速度、跟踪精度和对干扰的鲁棒性方面的有效性。

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引用次数: 0

Prescribed time performance control of underwater vehicles based on time-varying disturbance 基于时变扰动的水下航行器定时性能控制。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2026-01-01 DOI: 10.1016/j.isatra.2025.12.005

Lingyu Li , Hongde Qin , Hai Huang , Qiang Liu , Xiaojia Li

To enhance the robustness and control efficiency of underwater robot systems, this paper presents a controller that integrates model predictive control (MPC) with a prescribed time control, and introduces a novel time controller capable of more intuitively and effectively determining the convergence time of system performance. A radial basis function neural network (RBFNN) coupled with an evaluation neural network has been engineered to bolster the system's capacity to address ocean current disturbances. The introduction of a new error function ensures the effective realization of the neural network's stability. By incorporating a MPC outer loop controller with system state parameters, the system state can be transitioned smoothly and kept within bounds, aligning with the actual performance characteristics of underwater vehicles. The system's stability is verified through theoretical analysis and further substantiated through simulation experiments. Additionally, the performance enhancement of the robot system achieved by the proposed algorithm is emphasised through a comparative analysis with the traditional PID control algorithm. Ultimately, in light of the complex, time-varying external disturbance environment delineated in this paper, a sea trial experiment is conducted to ascertain the effectiveness of the proposed algorithm.

为了提高水下机器人系统的鲁棒性和控制效率，本文提出了一种将模型预测控制（MPC）与规定时间控制相结合的控制器，并引入了一种能够更直观有效地确定系统性能收敛时间的新型时间控制器。径向基函数神经网络（RBFNN）与评估神经网络相结合，增强了系统处理洋流干扰的能力。引入新的误差函数，保证了神经网络稳定性的有效实现。通过引入带系统状态参数的MPC外环控制器，使系统状态平稳过渡并保持在一定范围内，符合潜航器的实际性能特点。通过理论分析验证了系统的稳定性，并通过仿真实验进一步验证了系统的稳定性。此外，通过与传统PID控制算法的对比分析，强调了该算法对机器人系统性能的增强。最后，针对本文描述的复杂时变外部干扰环境，进行了海试实验，验证了所提算法的有效性。

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引用次数: 0

Design of zonotopic space interpolation-based smooth variable structure filter for systems with unknown but bounded noise 未知有界噪声系统的分区插值光滑变结构滤波器设计。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2026-01-01 DOI: 10.1016/j.isatra.2025.11.045

Lei-Ting Huo, Zi-Yun Wang, Yan Wang

In this paper, a zonotopic space interpolation-based smooth variable structure filter (ZSI-SVSF) is proposed for state estimation under unknown but bounded noise. The approach combines zonotopic contraction to determine a state confidence envelope with cubic spline interpolation to construct a smooth boundary layer. A curvature-driven mechanism then directs the smooth variable structure filter for iterative updates, adaptively computing the estimation gain and yielding a smooth trajectory. Unlike conventional filters, ZSI-SVSF avoids covariance computations, improving efficiency, reducing conservativeness, and mitigating chattering. Its applicability extends from linear systems to nonlinear measurements via Jacobian linearization. Validation through linear simulation with a hybrid motion model and nonlinear UAV tracking under a coordinated turn model confirms improved accuracy, adaptability, and low computational cost.

提出了一种基于分区空间插值的光滑变结构滤波器（ZSI-SVSF），用于未知有界噪声下的状态估计。该方法结合分位收缩确定状态置信度包络和三次样条插值构造光滑边界层。然后，曲率驱动机制指导光滑变结构滤波器进行迭代更新，自适应计算估计增益并产生光滑轨迹。与传统滤波器不同，ZSI-SVSF避免了协方差计算，提高了效率，降低了保守性，减轻了抖振。它的适用性从线性系统扩展到通过雅可比线性化的非线性测量。通过混合运动模型和协调转弯模型下的非线性无人机跟踪的线性仿真验证，该方法提高了精度、适应性和较低的计算成本。

引用次数: 0

Prescribed performance collision-free control for bearing-constrained unmanned aerial vehicle 轴承约束无人机规定性能无碰撞控制。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2026-01-01 DOI: 10.1016/j.isatra.2025.11.037

Tao Zhang , Dengxiu Yu , Kang Hao Cheong , Zhen Wang

This paper addresses the challenging problem of enabling bearing-constrained unmanned aerial vehicles to navigate through environments with multiple obstacles while strictly satisfying predefined convergence time and steady-state accuracy requirements. To meet the stringent payload and cost constraints of small-scale UAVs, we propose a lightweight local perception and localization framework that relies solely on angular (bearing) measurements, thereby minimizing sensor hardware requirements. An integrated obstacle-avoidance assistance mechanism is developed to ensure safe traversal in cluttered environments under these limited sensing conditions, without appreciably degrading the prescribed temporal and accuracy performance. Furthermore, a non-singular prescribed-performance control strategy is designed that seamlessly incorporates obstacle avoidance while guaranteeing user-specified convergence time and ultimate tracking precision. Finally, the effectiveness of the proposed approach is validated through multiple simulation examples.

本文解决了一个具有挑战性的问题，即在严格满足预定收敛时间和稳态精度要求的情况下，使受轴承约束的无人机在具有多个障碍物的环境中导航。为了满足小型无人机严格的有效载荷和成本限制，我们提出了一种轻量级的局部感知和定位框架，该框架仅依赖于角度（方位）测量，从而最大限度地减少了传感器硬件要求。开发了一种集成的避障辅助机制，以确保在这些有限的传感条件下在混乱环境中安全穿越，而不会明显降低规定的时间和精度性能。在保证用户指定的收敛时间和最终跟踪精度的前提下，设计了一种无缝融合避障的非奇异性能控制策略。最后，通过多个仿真实例验证了所提方法的有效性。

引用次数: 0

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