ISA transactions最新文献_第5页

Mask-based privacy-preserving adaptive bipartite fuzzy consensus control for stochastic nonlinear multi-agent systems under markovian switching topologies 马尔可夫切换拓扑下随机非线性多智能体系统基于掩码的保隐私自适应二部模糊一致性控制。

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

ISA transactions

Pub Date : 2026-02-01 DOI: 10.1016/j.isatra.2026.01.004

Xisheng Zhan , Xin Li , Jie Wu , Lingli Cheng , Huaicheng Yan

This paper studies an adaptive bipartite fuzzy consensus problem with privacy preservation in stochastic nonlinear multi-agent systems (SNMASs) under Markovian switching topologies. To handle unknown nonlinearities and protect sensitive information, a novel observer-based control strategy is proposed, in which adaptive fuzzy logic systems (FLSs) are employed to approximate unknown nonlinear functions and a vanishing affine mask function is designed to ensure the privacy of the agents’ initial states. A continuous-time Markov process governs stochastic topology changes, improving network robustness and adaptability. Theoretical analysis demonstrates that all signals of the closed-loop system are uniformly ultimately bounded in the mean-square sense, and practical bipartite consensus is achieved in the face of stochastic disturbances and nonlinearities. Notably, the proposed method is further extended to structurally unbalanced signed graphs by constructing a virtually balanced graph through pinning-type compensations, enabling the consensus protocol to operate directly on the original structurally unbalanced network while preserving convergence guarantees. Finally, the effectiveness of the proposed theoretical approach is validated through numerical simulations.

研究了马尔可夫切换拓扑下随机非线性多智能体系统（SNMASs）中具有隐私保护的自适应二部模糊一致问题。为了处理未知非线性并保护敏感信息，提出了一种新的基于观测器的控制策略，该策略采用自适应模糊逻辑系统（FLSs）逼近未知非线性函数，并设计了消失仿射掩模函数来保证智能体初始状态的隐私性。连续时间马尔可夫过程控制随机拓扑变化，提高网络的鲁棒性和适应性。理论分析表明，闭环系统的所有信号在均方意义上最终是一致有界的，并且在面对随机干扰和非线性时实现了实际的二部一致。值得注意的是，本文提出的方法进一步扩展到结构不平衡签名图，通过钉钉式补偿构造一个虚拟平衡图，使共识协议直接在原始结构不平衡网络上运行，同时保持收敛性保证。最后，通过数值仿真验证了所提理论方法的有效性。

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引用次数: 0

Dynamic-gain neural network observer based prescribed performance backstepping sliding mode control of uncertain nonlinear systems 基于动态增益神经网络观测器的不确定非线性系统预定性能反步滑模控制。

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

ISA transactions

Pub Date : 2026-02-01 DOI: 10.1016/j.isatra.2025.12.024

Linping Chan, Haiping Du, Chengxin Huo

This work presents a prescribed performance backstepping sliding mode control framework designed for a class of nonlinear systems with unknown disturbances. A key feature of the proposed method is the incorporation of a dynamic-gain neural network observer to handle system uncertainties and estimate unmeasurable states. In contrast with static gain observers, it adaptively adjusts its gain in real time, eliminating precise tuning. Moreover, an innovative integral nonsingular fast terminal sliding mode control (INFTSMC) strategy integrated with prescribed performance control (PPC) is developed to ensure that tracking errors adhere to pre-specified transient and steady-state requirements, enhancing reliability in practical applications. The control method manages dynamics, while the neural network observer compensates for nonlinearities, ensuring robustness under uncertainty. The system stability is analyzed via the Lyapunov theory. Simulation results demonstrate the effectiveness of the method.

针对一类具有未知扰动的非线性系统，提出了一种规定性能的反步滑模控制框架。该方法的一个关键特点是采用动态增益神经网络观测器来处理系统的不确定性和估计不可测状态。与静态增益观测器相比，它可以实时自适应调整其增益，消除了精确调谐。此外，提出了一种集成了规定性能控制（PPC）的创新积分非奇异快速终端滑模控制（INFTSMC）策略，以确保跟踪误差符合预先设定的瞬态和稳态要求，提高了实际应用中的可靠性。控制方法进行动态管理，神经网络观测器进行非线性补偿，保证了系统在不确定条件下的鲁棒性。利用李亚普诺夫理论分析了系统的稳定性。仿真结果验证了该方法的有效性。

引用次数: 0

Neuroadaptive fixed-time prescribed performance for full-state-constrained uncertain systems using dynamic surface control approach and its application 全状态约束不确定系统的神经自适应定时性能动态面控制方法及其应用。

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

ISA transactions

Pub Date : 2026-02-01 DOI: 10.1016/j.isatra.2025.12.021

Zhangbao Xu , Maokun Zhang , Jianyong Yao

In this article, precise control of a class of full-state constrained systems with uncertainty and unknown dynamics is studied. A neuroadaptive strategy is proposed to address unknown dynamics and parametric uncertainties. Moreover, disturbance observers are built to estimate unknown disturbances. Subsequently, a novel Lyapunov function incorporating an asymmetric prescribed performance function is constructed, ensuring that the tracking error converges to a small region within a fixed time. Furthermore, a neuroadaptive fixed-time prescribed performance controller with full-state constraints and disturbance compensation is developed, avoiding the use of tracking error transformation function in previous prescribed performance control and thus simplifying the controller design process. Moreover, dynamic surface technology is adopted to prevent the differential explosion problem generated in backstepping design. In addition, Lyapunov theory proves that the error system is locally ultimately exponentially bounded, and the asymmetric fixed-time prescribed tracking performance is guaranteed without violating any state constraints. Finally, the designed controller is tested by experiments.

研究了一类具有不确定性和未知动力学的全状态约束系统的精确控制问题。提出了一种神经自适应策略来解决未知动力学和参数不确定性。此外，还建立了干扰观测器来估计未知干扰。随后，构造了一个包含非对称规定性能函数的新型Lyapunov函数，保证了跟踪误差在固定时间内收敛到一个小区域。在此基础上，设计了一种具有全状态约束和扰动补偿的神经自适应定时预定性能控制器，避免了以往预定性能控制中跟踪误差变换函数的使用，简化了控制器的设计过程。此外，采用动态表面技术防止了反步设计中产生的差爆问题。此外，Lyapunov理论证明了误差系统是局部最终指数有界的，保证了不对称固定时间规定的跟踪性能不违反任何状态约束。最后，对所设计的控制器进行了实验验证。

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引用次数: 0

Data-based iterative learning control for nonlinear systems subject to iteration-dependent durations 基于数据的迭代学习控制与迭代相关的非线性系统。

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

ISA transactions

Pub Date : 2026-02-01 DOI: 10.1016/j.isatra.2025.12.034

Yuxin Wu , Deyuan Meng , Jian Sun

This paper addresses the data-based iterative learning control (ILC) problem for locally Lipschitz nonlinear systems, where the durations are iteration-dependent. A test framework is developed to perform test iterations for collecting specific input and output data from nonlinear ILC systems. By resorting to these data, an ILC updating law is provided through integrating modified outputs to compensate for the adverse effects of iteration-dependent durations. Thanks to the persistent full-learning property, a necessary and sufficient condition is proposed to accomplish the iteration-dependent perfect tracking objective, which depends on the output data. The developed ILC updating law that employs only data particularly applies to locally Lipschitz nonlinear ILC systems subject to irregular dynamics.

研究了基于数据的局部Lipschitz非线性系统的迭代学习控制问题，该系统的持续时间与迭代有关。开发了一个测试框架，用于执行从非线性ILC系统收集特定输入和输出数据的测试迭代。通过利用这些数据，通过整合修改后的输出来提供ILC更新规律，以补偿迭代依赖持续时间的不利影响。利用持续的全学习特性，提出了实现依赖于输出数据的迭代型完美跟踪目标的充分必要条件。所建立的仅使用数据的非线性惯性控制更新律特别适用于具有不规则动力学的局部Lipschitz非线性惯性控制系统。

引用次数: 0

Fixed-time sliding mode composite control with prescribed performance for uncertain Stewart parallel mechanism tracking in task space 任务空间不确定Stewart并联机构跟踪的定时滑模组合控制。

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

ISA transactions

Pub Date : 2026-02-01 DOI: 10.1016/j.isatra.2025.12.041

Yu Tang, Guoqin Gao

To improve the tracking control performance in task space of the Stewart parallel mechanism (SPM) with disturbances and uncertainties, a fixed-time sliding mode composite control with prescribed performance (FxT-SMCC-PP) method is developed. First, by constructing a sliding mode term adaptive to the changes of the lumped uncertainty, and embedding fixed-time performance parameters, a fixed-time adaptive sliding mode disturbance observer (FxTASMDO) is designed. Second, the SPM’s tracking error is constrained by an asymmetric hyperbolic cosecant prescribed performance function and transformed into a steady equivalent error by modifying the constraint transformation function. Based on the equivalent error, a fixed-time sliding mode control with prescribed performance is designed and combined with FxTASMDO to form the FxT-SMCC-PP, which can achieve the SPM’s fast and non-overshoot transient response, high steady-state tracking accuracy, and reduction in control energy consumption. Finally, the effectiveness of FxT-SMCC-PP is validated through simulation and prototype experiments.

为了提高具有干扰和不确定性的Stewart并联机构（SPM）在任务空间的跟踪控制性能，提出了一种规定性能的定时滑模复合控制方法（FxT-SMCC-PP）。首先，通过构造自适应集总不确定性变化的滑模项，并嵌入定时性能参数，设计了定时自适应滑模扰动观测器（FxTASMDO）。其次，将SPM的跟踪误差用非对称双曲余割规定性能函数进行约束，并通过修改约束变换函数将其转化为稳定的等效误差；基于等效误差，设计了一种具有规定性能的定时滑模控制，并与FxTASMDO结合构成FxT-SMCC-PP，实现了SPM快速无超调瞬态响应、高稳态跟踪精度和降低控制能耗。最后，通过仿真和样机实验验证了FxT-SMCC-PP的有效性。

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引用次数: 0

Adaptive performance enhancement control for flexible-joint manipulator with model uncertainties and actuator failures 具有模型不确定性和执行器失效的柔性关节机械臂自适应性能增强控制。

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

ISA transactions

Pub Date : 2026-02-01 DOI: 10.1016/j.isatra.2025.12.026

Hejia Gao , Yuanyuan Zhao , Chuanfeng He , Tanyu Chen , Changyin Sun

This paper focuses on flexible-joint robotic manipulators (FJRM), which possess numerous advantages such as high flexibility, precision, and fault-tolerance capabilities. However, FJRM are susceptible to various factors that may cause malfunctions during dynamic operations. These malfunctions not only compromise the operational stability and accuracy of the manipulator but also significantly shorten the equipment’s service life. To address these issues, developing an effective control strategy is of significant practical importance. This paper proposes a novel adaptive performance enhancement (APE) control method to effectively tackle model uncertainties and actuator failures in FJRM systems. An adaptive neural network (ANN) algorithm is designed to achieve accurate trajectory tracking of uncertain robotic systems by compensating for modeling errors. A non-singular terminal sliding mode (NTSM) policy is proposed to realize compliance control of robotic manipulators, which enhances the system’s robustness and interference suppression ability. The stability of the closed-loop system is subsequently validated using Lyapunov’s direct method. Finally, the effectiveness of the proposed control method is demonstrated through simulations and experiments conducted on the Gazebo simulation platform and the Baxter robot. Comparative analysis with fuzzy neural network (FNN), neural network (NN) and PD control methods further underscores the superiority of the proposed method in terms of control performance.

柔性关节机器人具有高柔性、高精度和容错能力等诸多优点。然而，在动态运行过程中，FJRM容易受到各种因素的影响，这些因素可能导致故障。这些故障不仅损害了机械手的运行稳定性和精度，而且大大缩短了设备的使用寿命。为了解决这些问题，制定有效的控制策略具有重要的实际意义。本文提出了一种新的自适应性能增强（APE）控制方法，有效地解决了FJRM系统中的模型不确定性和执行器失效问题。设计了一种自适应神经网络（ANN）算法，通过补偿建模误差实现不确定机器人系统的精确轨迹跟踪。提出了一种非奇异终端滑模（NTSM）策略来实现机器人的柔顺控制，增强了系统的鲁棒性和抗干扰能力。随后用李亚普诺夫直接法验证了闭环系统的稳定性。最后，在Gazebo仿真平台和Baxter机器人上进行了仿真和实验，验证了所提控制方法的有效性。通过与模糊神经网络（FNN）、神经网络（NN）和PD控制方法的对比分析，进一步强调了该方法在控制性能上的优越性。

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引用次数: 0

Multivariate Gaussian process-based learning model predictive control with unscented Kalman filter for autonomous surface vehicles 基于多元高斯过程的无气味卡尔曼滤波学习模型预测控制。

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

ISA transactions

Pub Date : 2026-02-01 DOI: 10.1016/j.isatra.2025.12.047

Zhi-Jie Wu, Li-Ying Hao

Modeling the nonlinear dynamics of autonomous surface vehicles (ASVs) is a complex challenge, driven by the intricate interplay of hydrodynamic effects and environmental uncertainties. In response to this challenge, this paper develops the system state and observation dynamics for ASVs using multivariate Gaussian process regression (MVGPR), then designs a learning-based model predictive control (MPC) scheme for trajectory tracking of ASVs. First, we introduce the application of MVGPR to model the ASVs dynamics, enabling accurate multi-input and multi-output correlation and uncertainty estimation, addressing the limitations of traditional Gaussian process regression (GPR) in high-dimensional settings. Based on the learned models, an unscented Kalman filter (UKF) is designed to improve state estimation accuracy through prior prediction and posterior updating, ensuring robustness even under unmeasurable states. Additionally, considering the impact of denial-of-service (DoS) attacks in communication networks, an MVGPR-based learning MPC framework is developed. By leveraging predictive capabilities, this framework eliminates the need for external compensators. The proposed method achieves robust and precise trajectory tracking while improving system stability under complex and uncertain maritime environments. Finally, the effectiveness of the proposed learning-based MPC algorithm is verified through comparative simulations and hardware experiments.

由于水动力效应和环境不确定性的复杂相互作用，自动水面车辆（asv）的非线性动力学建模是一项复杂的挑战。针对这一挑战，本文利用多元高斯过程回归（MVGPR）建立了自动驾驶汽车的系统状态和观测动力学模型，并设计了一种基于学习的自动驾驶汽车轨迹跟踪模型预测控制（MPC）方案。首先，我们引入了MVGPR对asv动态建模的应用，实现了精确的多输入多输出相关和不确定性估计，解决了传统高斯过程回归（GPR）在高维环境下的局限性。在学习模型的基础上，设计了一种无气味卡尔曼滤波器（unscented Kalman filter， UKF），通过先验预测和后验更新来提高状态估计的精度，保证在不可测状态下的鲁棒性。此外，考虑到通信网络中拒绝服务（DoS）攻击的影响，提出了一种基于mvgpr的学习MPC框架。通过利用预测功能，该框架消除了对外部补偿器的需求。该方法实现了鲁棒精确的轨迹跟踪，提高了系统在复杂不确定海洋环境下的稳定性。最后，通过对比仿真和硬件实验验证了基于学习的MPC算法的有效性。

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引用次数: 0

Co-design of dynamic event-triggered mechanism and observer-based control for networked systems under hybrid cyber attacks: A GA-LMI-based approach 混合网络攻击下网络系统动态事件触发机制和基于观察者控制的协同设计：基于ga - lmi的方法。

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

ISA transactions

Pub Date : 2026-02-01 DOI: 10.1016/j.isatra.2026.01.008

Tao Yu , Zhanpeng Wang , Zhiying Wu

This paper presents a co-design framework for an observer-based controller and a dynamic event-triggered mechanism (DETM) in networked control systems subject to hybrid cyber attacks. The hybrid attacks, including false data injection and replay attacks, are modeled using two independent Bernoulli processes. To reduce the network load a DETM is introduced between the sensor and controller with limited communication bandwidth. The closed-loop system’s stability and

H_{\infty}

performance are ensured through the derivation of nonlinear matrix inequalities. To address the nonlinear coupling in the co-design conditions, a variable transformation and matrix decomposition approach are employed to decouple the controller gains. However, this method relies on predefined DETM parameters, which may limit design flexibility. To overcome this issue a GA-LMI-based co-design algorithm is proposed to jointly optimize the controller and DETM parameters. Simulation results demonstrate that the proposed method achieves better

H_{\infty}

performance compared to some existing approaches. The effectiveness and low conservatism of the method are further validated through its application to a buck DC-DC converter system under hybrid cyber attacks.

本文提出了一种基于观测器的控制器和动态事件触发机制（DETM）的协同设计框架，用于受混合网络攻击的网络控制系统。混合攻击，包括假数据注入和重放攻击，使用两个独立的伯努利过程建模。在有限的通信带宽下，在传感器和控制器之间引入DETM以减少网络负载。通过非线性矩阵不等式的推导，保证了闭环系统的稳定性和H∞性能。为了解决协同设计条件下的非线性耦合问题，采用变量变换和矩阵分解方法对控制器增益进行解耦。然而，该方法依赖于预定义的DETM参数，这可能会限制设计的灵活性。为了克服这一问题，提出了一种基于ga - lmi的协同设计算法，对控制器和DETM参数进行联合优化。仿真结果表明，与现有方法相比，该方法具有更好的H∞性能。通过对混合网络攻击下降压DC-DC变换器系统的应用，进一步验证了该方法的有效性和低保守性。

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引用次数: 0

Self-triggered adaptive dynamic programming based on experience-replay and spectral adaptive law 基于经验重播和频谱自适应规律的自触发自适应动态规划。

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

ISA transactions

Pub Date : 2026-02-01 DOI: 10.1016/j.isatra.2025.12.008

Yuteng Tian , Xuemei Ren , Yongfeng Lv , Chao Zhang , Dongdong Zheng

In this paper, a novel self-triggered adaptive dynamic programming (ADP) framework is proposed, integrating experience replay (ER) and a spectral adaptive law (SPAL) for optimal control of unknown nonlinear systems. Firstly, in ADP-based optimal control solution methods, a critic neural network (NN) is often used to approximate its cost function, and we design a new critic NN weight updating law based on the SPAL, which improves the system’s generalization ability and makes it possible to solve the optimal control problem efficiently with different initial values. Then, a robust ADP method based on the ER technique is proposed in which a SPAL-based NN system identifier is used to provide data for ER, which systematically enhances the robustness of the ER-based ADP framework. Finally, since the sensors in the event-triggered ADP-based approach require continuous monitoring of the system state to compute the triggering conditions, to avoid this problem, we use a self-triggered mechanism that allows direct prediction of the next triggering moment based on the current state. The effectiveness and communication conservation of the proposed algorithm are verified by a simulation experiment.

针对未知非线性系统的最优控制问题，提出了一种新的自触发自适应动态规划（ADP）框架，该框架将经验重放（ER）和谱自适应律（SPAL）相结合。首先，在基于adp的最优控制求解方法中，经常使用评论家神经网络（NN）来逼近其成本函数，并设计了一种新的基于SPAL的评论家神经网络权值更新律，提高了系统的泛化能力，使得在不同初始值的情况下有效地求解最优控制问题成为可能。然后，提出了一种基于ER技术的鲁棒ADP方法，该方法利用基于spal的神经网络系统辨识器为ER提供数据，系统地增强了基于ER的ADP框架的鲁棒性。最后，由于基于事件触发adp方法中的传感器需要连续监测系统状态来计算触发条件，为了避免这个问题，我们使用了一种自触发机制，允许根据当前状态直接预测下一个触发时刻。仿真实验验证了该算法的有效性和通信守恒性。

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引用次数: 0

Fast integral terminal synchronous sliding mode control for pantograph robots 受电弓机器人快速积分终端同步滑模控制。

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

ISA transactions

Pub Date : 2026-02-01 DOI: 10.1016/j.isatra.2025.12.016

Muhammad Ali Hassan , Zhenwei Cao , Kamal Rsetam , Yusai Zheng , Zhihong Man

In this paper, a fast integral terminal synchronous sliding mode control (FITSSMC) is newly proposed for a 2-degree-of-freedom (2-DOF) pantograph robot (PR). Firstly, the mathematical modeling of the PR is established using inverse kinematics, forward kinematics, and servo motor dynamics. Secondly, the FITSSMC is developed based on the norm-normalized sign function (NNSF) to guarantee the fast synchronous convergence of the position tracking errors of the two servo motors that the PR consists of. Based on the novel exponential piecewise functions, the sliding surface and the reaching law of the proposed FITSSMC are constructed for fast tracking error convergence. Moreover, an integral term is utilized in the proposed FITSSMC to ensure singularity avoidance and strong robustness towards external disturbances. To estimate the unmeasurable states of both servo motors in the PR, two finite-time state observers (FTSOs) are designed. Thirdly, the synchronous and finite-time stability of the PR control system under the FITSSMC is analyzed using the Lyapunov method. Thus, the proposed control scheme is able to achieve the fast synchronous convergence and minimize the synchronous position tracking errors of both motors of the PR. Finally, the comparative simulation and experimental results on the PR demonstrate the superior performance of the proposed control with the fast synchronous convergence and better tracking accuracy in the presence of uncertainties and external disturbances.

针对2自由度受电弓机器人（PR），提出了一种快速积分末端同步滑模控制方法。首先，利用逆运动学、正运动学和伺服电机动力学建立了机器人的数学模型。其次，基于范数归一化符号函数（NNSF）开发了FITSSMC，以保证由两个伺服电机组成的位置跟踪误差的快速同步收敛；基于新的指数分段函数，构造了FITSSMC的滑动面和趋近律，实现了快速跟踪误差收敛。此外，所提出的FITSSMC采用积分项来避免奇异性，并对外界干扰具有较强的鲁棒性。设计了两个有限时间状态观测器（ftso）来估计两种伺服电机的不可测状态。第三，利用Lyapunov方法分析了FITSSMC下PR控制系统的同步稳定性和有限时间稳定性。因此，所提出的控制方案能够实现快速的同步收敛，使双电机同步位置跟踪误差最小化。最后，通过对双电机的仿真和实验对比，验证了所提出的控制方案在存在不确定性和外界干扰的情况下具有快速的同步收敛和较好的跟踪精度。

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引用次数: 0