ISA transactions最新文献

英文中文

Looped functional approach to finite-horizon hybrid control for networked switched fuzzy systems with fault indicators and cyber attacks 具有故障指示和网络攻击的网络交换模糊系统有限视界混合控制的环泛函方法。

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.2025.12.014

N. Aravinth , R. Sakthivel , O.M. Kwon

The presented research effort offers a secure sampled hybrid control design for networked switched fuzzy systems. Precisely, we investigate the challenges posed by actuator failures, hybrid cyber attacks, time-varying delays and external disturbances over the finite horizon. A unified control design that effectively handles these simultaneous factors in a single framework is an area that warrants investigation. In this connection, a hybrid control law which comprises the robust and fault-tolerant control is established. One of the primary appealing aspects of the designed control law is that it employs robust control when the system is free of faults and switches to the fault-tolerant one when the system encounters a fault. Further, by considering the state information from

ϑ (t_{k})

ϑ (t)

and

ϑ (t)

ϑ (t_{k + 1})

, a two-sided looped functional is developed. Based on that, adequate conditions that promise the core objective of this examination are formulated in terms of linear matrix inequality. Apart from this, to escalate the controller functionality in regard to cyber threats, hybrid cyber attacks that comprise the deception and denial-of-service attacks are taken into account and Bernoulli distribution is utilized to govern the random characteristics of the attack. Additionally, to address the repercussions of the external disturbance, extended dissipative theory is deployed. In the closing part, two examples, including the single-link robotic arm model accompanied by the simulation findings, are presented to showcase the capability and applicability of the achieved theoretical outcomes and control design.

本文的研究为网络交换模糊系统提供了一种安全的采样混合控制设计。准确地说，我们研究了执行器故障、混合网络攻击、时变延迟和有限范围内的外部干扰所带来的挑战。在单一框架中有效处理这些同时发生的因素的统一控制设计是值得研究的领域。在此基础上，建立了鲁棒控制和容错控制相结合的混合控制律。所设计的控制律的一个主要吸引人的方面是，当系统没有故障时，它采用鲁棒控制，当系统遇到故障时，它切换到容错控制。进一步，通过考虑从φ (tk)到φ (t)和φ (t)到φ (tk+1)的状态信息，建立了一个双边环泛函。在此基础上，以线性矩阵不等式的形式表述了保证本研究核心目标的充分条件。除此之外，为了提升控制器在网络威胁方面的功能，考虑了包含欺骗和拒绝服务攻击的混合网络攻击，并利用伯努利分布来控制攻击的随机特征。此外，为了解决外部干扰的影响，扩展耗散理论被部署。最后，以单连杆机械臂模型为例，结合仿真结果，展示了所获得的理论成果和控制设计的能力和适用性。

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引用次数: 0

Prescribed-time fully distributed optimization for time-varying costs: Zero-gradient-sum scheme 时变成本的规定时间全分布优化：零梯度和方案。

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.2026.01.007

Ningning Mao , Shuai Liu , Yuan Liu

This article introduces a novel distributed optimization algorithm for time-varying objectives that achieves prescribed-time convergence. The method is developed based on zero-gradient-sum (ZGS) principles and employs a sliding-mode control framework. Specifically, the controller is designed to satisfy the ZGS condition within a prescribed time horizon, ensuring exact convergence by the user-specified deadline. A key contribution of this research is the introduction of adaptive parameters with time-varying scaling functions. This innovation addresses a fundamental limitation in existing methods by eliminating their dependency on global network information. Consequently, the algorithm achieves truly distributed control without requiring knowledge of Laplacian matrix eigenvalues. The algorithm offers significant advantages including complete freedom from initial condition constraints and local minimization requirements, full independence from global topological information, and rigorous prescribed-time convergence guarantees. Theoretical analysis establishes the convergence properties through an integrated approach that combines optimization theory with Lyapunov stability analysis. Numerical simulations demonstrate the algorithm’s effectiveness and superior performance in handling time-varying optimization problems compared to existing approaches.

本文介绍了一种新的时变目标分布式优化算法，该算法能达到规定的时间收敛性。该方法基于零梯度和（ZGS）原理，采用滑模控制框架。具体来说，控制器被设计为在规定的时间范围内满足ZGS条件，确保在用户指定的截止日期前精确收敛。本研究的一个关键贡献是引入了具有时变尺度函数的自适应参数。这一创新消除了现有方法对全球网络信息的依赖，从而解决了现有方法的一个基本限制。因此，该算法在不需要了解拉普拉斯矩阵特征值的情况下，实现了真正意义上的分布式控制。该算法具有完全不受初始条件约束和局部最小化要求的约束、完全独立于全局拓扑信息、严格的规定时间收敛保证等显著优点。理论分析采用优化理论与李雅普诺夫稳定性分析相结合的方法，建立了算法的收敛性。数值仿真结果表明，该算法在处理时变优化问题时具有较好的有效性和性能。

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引用次数: 0

An online reinforcement learning-based conflict resolution approach for human–vehicle cooperative systems 基于在线强化学习的人车合作系统冲突解决方法。

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.2025.12.017

Viet Thang Dang , Duy Lam Mac , Duc Thinh Le , Tung Lam Nguyen , Van Trong Dang

Conflicts between driver and automation remain a key challenge in cooperative driving systems, as prolonged differences in intentions can degrade human experience, reduce trust, and compromise driving safety. In this paper, a conflict resolution approach is proposed for human–vehicle cooperative systems to minimize errors in trajectory tracking and conflicts between driver and automation. By considering driver-in-the-loop, a vehicle-driver-conflict model is introduced, and then an online reinforcement learning algorithm is developed to calculate the automated steering angle for cooperative systems, which can assist humans even in conflict situations. Based on Lyapunov’s theory, the states of the vehicle-driver-conflict model are guaranteed to be uniformly ultimately bounded and the convergence of the control signal and cost function to their optimal values is verified. Finally, the effectiveness of the proposed method is demonstrated and compared with other methods through numerical simulations.

驾驶员和自动驾驶系统之间的冲突仍然是合作驾驶系统面临的主要挑战，因为长期的意图差异会降低人类的体验，减少信任，并危及驾驶安全。本文提出了一种针对人车协同系统的冲突解决方法，以最大限度地减少轨迹跟踪误差和驾驶员与自动化之间的冲突。在考虑驾驶员在环的情况下，引入了车辆-驾驶员-冲突模型，并开发了一种在线强化学习算法来计算协作系统的自动转向角度，使其在冲突情况下也能辅助人类。基于Lyapunov理论，保证了车辆-驾驶员-冲突模型的状态最终一致有界，并验证了控制信号和代价函数收敛于它们的最优值。最后，通过数值仿真验证了该方法的有效性，并与其他方法进行了比较。

引用次数: 0

Tracking accuracy of cyber-physical systems over bandwidth-limited channel 网络物理系统在有限带宽信道上的跟踪精度。

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.2025.12.027

Xian-He Zhang , Bo Li , Xiao-Wei Jiang , Wenxing Wang , Shaoying Wang

Taking the

H_{2}

norm of the tracking error signal as the performance index, this study examines the output tracking performance limits of networked systems with bandwidth-limited communication channels. Two linear time-invariant filters are used to characterize the bandwidth constraints and additive colored Gaussian noise (ACGN), respectively. By invoking some factorization techniques and Youla parameterization of two-parameter controllers, the fundamental tracking performance limitations are achieved. From the exact expressions, it is evident that there exists a close relationship between performance limitations and the essential features of the plant. Further, it is also concluded that the limited bandwidth and ACGN generally have adverse effects on tracking performance. Finally, the theoretical results are verified through a practical plant example. For a linear dual-freedom vehicle system, the results also show the effectiveness of the proposed method.

本文以跟踪误差信号的H2范数作为性能指标，考察了通信信道带宽有限的网络化系统的输出跟踪性能极限。使用两个线性时不变滤波器分别表征带宽约束和加性有色高斯噪声。通过调用一些因子分解技术和双参数控制器的Youla参数化，实现了基本的跟踪性能限制。从确切的表述可以明显看出，在性能限制和植物的基本特征之间存在着密切的关系。此外，有限的带宽和ACGN通常会对跟踪性能产生不利影响。最后，通过实例对理论结果进行了验证。对于线性双自由度车辆系统，结果也表明了所提方法的有效性。

引用次数: 0

Affine formation maneuver control for multi-agent systems against false data injection attacks based on adaptive dual-terminal dynamic triggering 基于自适应双端动态触发的多智能体系统防假数据注入仿射编队机动控制

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.2025.12.023

Yuezheng Xie , Chenjun Liu , Jason J.R. Liu

This study develops an adaptive dual-terminal event-triggered control framework for secure affine formation maneuvering in multi-agent systems under false data injection (FDI) attacks. The framework integrates stress matrix-based affine localization with adaptive attack compensation, enabling followers to reconstruct attack-free states. A dual-channel triggering mechanism with time-dependent thresholds dynamically regulates communication based on state and input errors, balancing network load reduction with stability. Lyapunov analysis confirms bounded tracking errors and exclusion of Zeno behavior. Validation demonstrates effective attack mitigation, formation integrity preservation, robust performance, and communication efficiency.

针对虚假数据注入（FDI）攻击下多智能体系统的安全仿射编队机动，开发了一种自适应双端事件触发控制框架。该框架将基于应力矩阵的仿射定位与自适应攻击补偿相结合，使跟踪器能够重建无攻击状态。基于状态和输入错误的双通道触发机制与时间相关的阈值动态调节通信，平衡网络负载减少与稳定性。李亚普诺夫分析证实了有界跟踪误差和芝诺行为的排除。验证证明了有效的攻击缓解、地层完整性保持、健壮的性能和通信效率。

引用次数: 0

Reinforcement learning-based pitch control for wind turbines using double deep Q-networks 基于强化学习的双深q网络风力发电机俯仰控制。

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.2026.01.015

Víctor Espinoza , Carolina Ormaza , Christian Tutivén , Yolanda Vidal

This paper presents a novel reinforcement learning-based framework for pitch control in wind turbines, addressing the challenges posed by high nonlinearity and uncertainties in wind dynamics. Traditional pitch control methods, such as proportional-integral controllers, often struggle with adaptability and load mitigation under fluctuating wind conditions. To enhance control efficiency, the proposed framework employs a two-stage approach: policy transfer and policy refinement. In the policy transfer stage, a proportional-integral controller is used to estimate an initial control policy. This policy is then refined using the double deep Q-Network algorithm. Furthermore, a novel reward function is introduced as an improved version of a recent formulation in the literature, ensuring better alignment with practical operational scenarios. Simulations demonstrate that the proposed controller outperforms the industry-standard ROSCO controller, achieving a 15.87% reduction in power fluctuations, while preserving comparable structural load levels, with slight reductions in some cases. These results highlight the potential of reinforcement learning-based control to enhance wind turbine power regulation without compromising structural safety in terms of loads and vibrations.

本文提出了一种新的基于强化学习的风力发电机俯仰控制框架，解决了风力力学中高度非线性和不确定性带来的挑战。传统的俯仰控制方法，如比例积分控制器，在波动风条件下往往难以适应和减轻负荷。为了提高控制效率，该框架采用了两阶段的方法：策略转移和策略细化。在策略转移阶段，采用比例积分控制器对初始控制策略进行估计。然后使用双深度Q-Network算法对该策略进行细化。此外，本文还引入了一种新的奖励函数，作为文献中最近公式的改进版本，以确保更好地与实际操作场景保持一致。仿真表明，所提出的控制器优于行业标准的ROSCO控制器，在保持可比结构负载水平的同时，实现了15.87%的功率波动降低，在某些情况下略有降低。这些结果突出了基于强化学习的控制的潜力，可以在不影响负载和振动方面的结构安全的情况下增强风力涡轮机的功率调节。

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引用次数: 0

Bumpless transfer for distributed generation units with mode-dependent controllers 具有模式依赖控制器的分布式发电机组的无颠簸传输。

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.2025.12.048

Pablo.E. Muñoz , Ricardo.J. Mantz , Sergio.A. González

Distributed generation units (DGUs) in grid-connected microgrids are typically controlled as current sources. Nevertheless, when the microgrids are isolated, some DGUs must switch their control law to operate as voltage sources. Switching between controllers implies unwanted transients at the DGUs output power due to mismatches between controller’s outputs, challenging both voltage and frequency transients and potentially leading to instability. This work proposes a bumpless-transfer scheme based on sliding-mode reference conditioning for the DGUs to achieve a smooth transition between grid-connected and island modes in electrical microgrids. By guaranteeing the establishment of a sliding regime, the suggested bumpless algorithm reduces to zero the mismatch between the DGU controllers’ outputs when the switch is performed. In this manner, jumps in the control action and the associated unwanted transients are avoided regardless of when the control law switches. Results show good and robust performance in the face of both islanding and reconnection processes.

并网微电网中的分布式发电机组通常作为电流源进行控制。然而，当微电网处于隔离状态时，一些dgu必须切换其控制律作为电压源运行。控制器之间的切换意味着由于控制器输出之间的不匹配，在DGUs输出功率上存在不必要的瞬态，挑战电压和频率瞬态，并可能导致不稳定。本文提出了一种基于滑模参考调节的无碰撞转换方案，以实现微电网并网模式和孤岛模式之间的平滑过渡。通过保证滑动状态的建立，建议的无颠簸算法在切换时将DGU控制器输出之间的不匹配减小到零。通过这种方式，无论控制律何时切换，都可以避免控制动作中的跳跃和相关的不必要的瞬变。结果表明，该方法在孤岛和重连接过程中都具有良好的鲁棒性。

引用次数: 0

Fault-tolerant control of discrete-time markov jump power systems based on mode detection information 基于模式检测信息的离散马尔可夫跳变电力系统容错控制。

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.2026.01.002

Shen Zhang, Wenhao Zhang, Lei Su, Tian Fang, Kang Wang

This paper studies the fault-tolerant control of Markov jump power systems. In practical engineering, it is challenging to obtain accurate modal data of power systems and describe circuit breaker switching phenomena. To address this, a hidden Markov jump power systems model is established. In order to cope with the challenges of possible controller failures and incomplete modal information acquisition, fault-tolerant control strategies and partially unknown probability matrices are introduced. Based on the Lyapunov stability theory, sufficient conditions are obtained to ensure that the closed-loop system is stochastically stable and meets the desired

H_{\infty}

performance. Finally, control design for a single-machine infinite bus power system demonstrates that stable operation is preserved under both incomplete modal information and concurrent faults.

研究了马尔可夫跳变电力系统的容错控制问题。在实际工程中，获取准确的电力系统模态数据和描述断路器的开关现象是一项具有挑战性的工作。为了解决这一问题，建立了隐马尔可夫跳变电力系统模型。为了应对控制器可能失效和模态信息获取不完全的挑战，引入了容错控制策略和部分未知概率矩阵。基于Lyapunov稳定性理论，得到了保证闭环系统随机稳定并满足期望H∞性能的充分条件。最后，通过对单机无限母线电力系统的控制设计，验证了该系统在模态信息不完全和并发故障情况下都能保持稳定运行。

引用次数: 0

Optimized neural-network-assisted model-based motion control of hydraulic manipulators under unmodeled uncertainties 未建模不确定条件下液压机械臂的优化神经网络辅助模型运动控制。

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.2025.12.055

Manzhi Qi , Yangxiu Xia , Shizhao Zhou , Litong Lyu , Zheng Chen

Multi-degree-of-freedom (multi-DoF) hydraulic manipulators are widely used in heavy-duty tasks due to their high power density. However, their inherent nonlinearities and uncertain dynamics present significant challenges to precise control. Traditional model-based control methods, while effective in addressing these nonlinearities, rely heavily on accurate dynamic models, which complicates controller design and increases computational demands. To address these limitations, this paper proposes a neural-network-assisted adaptive robust control approach that reduces the dependence on precise modeling by compensating for unmodeled dynamics. Radial basis function neural networks (RBFNNs) are employed to approximate the uncertain dynamics, with the network structure optimized using the K-means

+ +

algorithm to enhance approximation accuracy and computational efficiency. Additionally, desired signals are used instead of measured signals to mitigate sensitivity to measurement noise. The proposed method is rigorously analyzed to guarantee the stability and asymptotic tracking performance of the closed-loop system. Experimental results on a hydraulic manipulator validate the effectiveness of the approach, demonstrating substantial improvements in control performance compared with conventional model-based strategies.

多自由度液压机械臂因其高功率密度而广泛应用于重载作业中。然而，它们固有的非线性和不确定性给精确控制带来了巨大的挑战。传统的基于模型的控制方法虽然可以有效地解决这些非线性问题，但严重依赖于精确的动态模型，这使得控制器设计变得复杂并增加了计算量。为了解决这些限制，本文提出了一种神经网络辅助的自适应鲁棒控制方法，通过补偿未建模的动力学来减少对精确建模的依赖。采用径向基函数神经网络（RBFNNs）逼近不确定动力学，并采用k -means++算法优化网络结构，提高逼近精度和计算效率。此外，期望信号被用来代替测量信号，以减轻对测量噪声的敏感性。对所提出的方法进行了严格的分析，保证了闭环系统的稳定性和渐近跟踪性能。在液压机械臂上的实验结果验证了该方法的有效性，与传统的基于模型的控制策略相比，该方法的控制性能有了实质性的提高。

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引用次数: 0

Cooperative control for FWID-EVs with active suspension under extreme conditions by using off-policy safe reinforcement learning 基于非策略安全强化学习的fwidd - ev主动悬架极端工况协同控制

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.2026.01.010

Zhijiang Gao, Pak Kin Wong, Fuqiu Zhou, Jing Zhao

The unbalanced distribution of tyre normal forces induced by lateral load transfer poses a significant threat to vehicle handling stability under extreme conditions. However, the mitigation of such imbalance is inherently constrained by the suspension support capability. This work proposes a novel off-policy safe reinforcement learning-based cooperative (OSRC) controller that coordinates direct yaw control (DYC) and active suspension system (ASS) to optimize the distribution of tyre normal forces and enhance the handling stability of the four-wheel independent drive electric vehicle (FWID-EV) under extreme conditions. First, a DYC-ASS cooperative architecture is proposed to optimize the distribution of tyre normal forces in the FWID-EV system. Subsequently, a combined tyre-slip-angle and phase-portrait (CTP) method is proposed to characterize the relationship between the tyre force saturation and the lateral stability region (LSR). Based on a two-player game, the OSRC controller is proposed to achieve the cooperation between the DYC and the ASS and enhance the handling stability of the FWID-EV under extreme conditions. Finally, the effectiveness of the proposed OSRC controller is examined via the rapid control prototyping tests.

侧向载荷传递引起的轮胎法向力的不平衡分布对极端工况下车辆的操纵稳定性构成重大威胁。然而，这种不平衡的缓解受到悬架支撑能力的固有约束。本文提出了一种新型的非策略安全强化学习协同（OSRC）控制器，该控制器协调直接偏航控制（DYC）和主动悬架系统（ASS），以优化轮胎法向力的分布，提高四轮独立驱动电动汽车（FWID-EV）在极端条件下的操纵稳定性。首先，提出DYC-ASS协同架构，优化FWID-EV系统中轮胎法向力的分布；随后，提出了轮胎滑移角与相位画像相结合的方法来表征轮胎力饱和度与横向稳定区（LSR）的关系。为了实现DYC与ASS的协同工作，提高fwidd - ev在极端工况下的操控稳定性，提出了基于二人博弈的OSRC控制器。最后，通过快速控制原型测试验证了所提出的OSRC控制器的有效性。

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