ISA transactions最新文献_第5页

Time-varying sliding mode control based finite-time prescribed performance function for robotic manipulators 基于有限时间规定性能函数的机械臂时变滑模控制。

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.2025.11.024

Sana Stihi , Raouf Fareh , Sofiane Khadraoui , Maamar Bettayeb , Mohamed Tadjine

Sliding mode control (SMC) is valued for its robustness and capacity to handle uncertainties in robot-manipulator applications that require precise tracking. However, it is limited by chattering, and starting far from the sliding surface can lead to extended reaching phases, compromising the global control efficacy and robustness. While Time-Varying Sliding Mode Surfaces (TVSMS) have been proposed to eliminate the reaching phase, they often suffer from sensitivity to initial conditions and parameter selection, limiting precise finite-time error convergence. Ensuring robustness during the reaching and sliding phases while achieving finite-time convergence from any initial position is a challenging task. This study presents a novel approach by integrating a Finite-Time Prescribed Performance Function (FTPPF) into a TVSMS design. The proposed TVSMS, based on FTPPF, ensures error convergence within a predetermined time frame, eliminates the reaching phase, and reduces sensitivity to initial conditions. Furthermore, the designed TVSMS addresses the weakness of robustness during the reaching phase of the Power Rate Reaching Law (PRRL) employed in the control law design, thereby mitigating the chattering problem of the SMC. Three FTPPFs with minimal parameter tuning are introduced, offering flexible transient response shaping, robustness, and improved error convergence compared to traditional TVSMS. The proposed Time-Varying Sliding-Mode Controller (TVSMC) not only simplifies control implementation but also significantly enhances robustness and resilience to external disturbances, making it a promising solution for high-precision robotic applications. Finite-time stability analysis is validated using the Lyapunov theorem, and experimental validation on the MICO 4-DOF robot demonstrates superior performance across various case studies compared to conventional methods.

滑模控制（SMC）因其鲁棒性和处理不确定性的能力在需要精确跟踪的机器人-机械臂应用中受到重视。然而，它受到抖振的限制，并且从远离滑动表面开始会导致到达相位延长，从而影响全局控制效果和鲁棒性。虽然时变滑模曲面（TVSMS）已经被提出用于消除到达相位，但它们往往对初始条件和参数选择敏感，限制了精确的有限时间误差收敛。确保在到达和滑动阶段的鲁棒性，同时从任何初始位置实现有限时间收敛是一项具有挑战性的任务。本研究提出了一种将有限时间规定性能函数（FTPPF）整合到TVSMS设计中的新方法。提出的基于FTPPF的TVSMS确保了误差在预定时间范围内收敛，消除了到达相位，降低了对初始条件的敏感性。此外，所设计的TVSMS解决了在控制律设计中采用的功率趋近律（PRRL）到达阶段的鲁棒性不足，从而减轻了SMC的抖振问题。介绍了三种具有最小参数调谐的ftppf，与传统的TVSMS相比，它们具有灵活的瞬态响应整形、鲁棒性和改进的误差收敛性。所提出的时变滑模控制器（TVSMC）不仅简化了控制实现，而且显著提高了对外部干扰的鲁棒性和弹性，使其成为高精度机器人应用的一个有前途的解决方案。利用李雅普诺夫定理验证了有限时间稳定性分析，并在MICO 4-DOF机器人上进行了实验验证，与传统方法相比，在各种案例研究中显示了优越的性能。

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引用次数: 0

Event-triggered adaptive tracking control for USV based on enhanced optimized backstepping technique 基于增强优化反演技术的USV事件触发自适应跟踪控制。

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.2025.11.047

Hugan Zhang , Xianku Zhang , Yongjin Liu , Shihang Gao , Daocheng Ma

This study addresses key challenges in the trajectory tracking control of unmanned surface vehicles, including external environmental disturbances, excessive sensor communication burdens, and complex controller design. An enhanced adaptive optimal backstepping control method based on an event-triggered mechanism is proposed. The approach employs an actor-critic reinforcement learning framework, in which the critic network performs online evaluation of system performance and the actor network generates optimal control decisions. Neural networks are designed to estimate the error gradient of the cost function, thereby simplifying the complex matrix differentiation process required in conventional methods. Additionally, an event-triggered mechanism is introduced to significantly reduce sensor communication frequency, while a disturbance observer is developed to estimate and compensate for environmental disturbances in real time, thus enhancing system robustness. Theoretical analysis establishes the stability and effectiveness of the proposed algorithm, and simulation results verify its superior performance.

该研究解决了无人水面车辆轨迹跟踪控制中的关键挑战，包括外部环境干扰、传感器通信负担过重以及复杂的控制器设计。提出了一种基于事件触发机制的增强自适应最优反演控制方法。该方法采用了一个行为者-批评家强化学习框架，其中批评家网络对系统性能进行在线评估，行为者网络生成最优控制决策。神经网络用于估计代价函数的误差梯度，从而简化了传统方法中复杂的矩阵微分过程。此外，引入了事件触发机制，显著降低了传感器通信频率，同时开发了干扰观测器，实时估计和补偿环境干扰，从而增强了系统的鲁棒性。理论分析证明了该算法的稳定性和有效性，仿真结果验证了其优越的性能。

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引用次数: 0

Contrast-enhanced adversarial domain generalization network with data augmentation and Bayesian inference for imbalanced bearing fault diagnosis 基于数据增强和贝叶斯推理的对比增强对抗域泛化网络不平衡轴承故障诊断。

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.2025.11.021

Rui Liu , Jimeng Li , Xilei Guan , Chenbo Jia , Jinfeng Zhang

Advanced fault diagnosis techniques typically rely on large amounts of labeled data from known domains, yet collected datasets often suffer from significant class imbalance. In addition, unseen domain data, which arises from variations in operating conditions or equipment heterogeneity, usually lacks sufficient prior knowledge, leading to a marked decline in model performance. To address these issues, this study proposes a contrast-enhanced adversarial domain generalization framework that integrates data augmentation and Bayesian inference for imbalanced fault diagnosis of rolling bearings under diverse scenarios. Specifically, a correlation-guided adaptive mixup method is developed to alleviate class imbalance in source domains by adaptively adjusting sample weights according to their similarity. A feature extractor based on multiscale pinwheel-shaped convolutions and spatial-channel collaborative attention, together with a parallel multi-classifier training architecture, is then designed to enhance feature learning from multiple source domains. To further strengthen generalization, an inter-domain contrastive loss is incorporated into adversarial training, encouraging the model to capture more robust domain-invariant representations. Finally, a Bayesian fusion mechanism with dynamic weighting is introduced to integrate the complementary strengths of different classifiers for accurate recognition of unseen domain data. Two rolling bearing datasets are employed to construct cross-condition and cross-machine experimental tasks. Comparative results demonstrate that the proposed approach achieves superior diagnostic accuracy and strong generalization capability, thus providing a reliable solution for industrial equipment health monitoring.

先进的故障诊断技术通常依赖于来自已知领域的大量标记数据，但所收集的数据集往往存在显著的类不平衡。此外，由于操作条件的变化或设备的异质性而产生的未知领域数据通常缺乏足够的先验知识，导致模型性能明显下降。为了解决这些问题，本研究提出了一种结合数据增强和贝叶斯推理的对比增强对抗域泛化框架，用于不同场景下滚动轴承不平衡故障诊断。具体而言，提出了一种关联引导的自适应混合方法，通过自适应调整样本的相似度来缓解源域的类不平衡。然后，设计了基于多尺度风车形状卷积和空间通道协同关注的特征提取器，以及并行多分类器训练架构，以增强多源域的特征学习。为了进一步加强泛化，将域间对比损失纳入对抗训练中，鼓励模型捕获更鲁棒的域不变表示。最后，引入动态加权贝叶斯融合机制，整合不同分类器的互补优势，实现对未知领域数据的准确识别。使用两个滚动轴承数据集构建跨条件和跨机器的实验任务。对比结果表明，该方法具有较高的诊断精度和较强的泛化能力，为工业设备健康监测提供了可靠的解决方案。

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引用次数: 0

Nonlinear robust control of pump controlled single-rod actuator with observing and compensating modeling uncertainties 基于模型不确定性观察与补偿的泵控单杆作动器非线性鲁棒控制。

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.2025.10.053

Xinhao Ji , Yuexing Wang , Haibo Xie , Chengzhen Wang

High-precision control technology for the pump controlled single-rod actuator (PCSA) system is in high demand in industrial applications. In the PCSA system, there are both matched and mismatched uncertainties. However, it is still a challenging task for existing observer-based controllers to simultaneously observe and compensate for the uncertainty of mismatched and matched. To solve this issue, in this article, a modeling-uncertainty-observer based nonlinear robust control strategy was designed. The working mechanism of the proposed method is to observe the uncertainties from measurable state variables and then, based on the uncertainties observation, take feedforward control to compensate for the uncertainties. The developed observer can simultaneously observe and compensate for the uncertainties of mismatched and matched. Meanwhile, to improve transient response, the robust control technology was employed to deal with the compensation error. Comparative experiments show that the proposed method can effectively estimate and compensate for the model uncertainty, achieving the prescribed control accuracy. Compared with the robust controller and PID controller, the proposed method reduces the maximum control error by more than 20 % and 61 %, respectively.

泵控单杆执行器（PCSA）系统的高精度控制技术在工业应用中有很高的需求。在PCSA系统中，既有匹配不确定性，也有不匹配不确定性。然而，对于现有的基于观测器的控制器来说，如何同时观察和补偿不匹配和不匹配的不确定性仍然是一个具有挑战性的任务。为了解决这一问题，本文设计了一种基于建模-不确定性-观测器的非线性鲁棒控制策略。该方法的工作原理是观察可测状态变量的不确定性，并在此基础上采用前馈控制对不确定性进行补偿。开发的观测器可以同时观测和补偿不匹配和不匹配的不确定性。同时，为了改善系统的暂态响应，采用鲁棒控制技术对补偿误差进行处理。对比实验表明，该方法能有效地估计和补偿模型的不确定性，达到了规定的控制精度。与鲁棒控制器和PID控制器相比，该方法的最大控制误差分别降低了20% %和61% %以上。

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引用次数: 0

Dynamic event-triggered fault-tolerant control of rigid spacecraft with prescribed performance 给定性能的刚性航天器动态事件触发容错控制。

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.2025.10.045

Peng Cheng , Wenjun Luo , Jason J.R. Liu , Zhiguang Feng

This study proposes a fixed-time disturbance observer (DO)-based dynamic event-triggered fault-tolerant control (DETFTC) framework with prescribed performance for the attitude tracking of rigid spacecraft hampered by actuator faults, parameter uncertainties, and environmental disturbances. In contrast to conventional DO approaches that rely on initial conditions or upper disturbance bounds, this work introduces a fixed-time DO to accurately reconstruct lumped perturbations arising from exogenous disturbances, inertia uncertainties, and actuator faults. A DETFTC strategy is further developed to minimize unnecessary control signal updates while ensuring rapid and precise attitude tracking. Integrating fixed-time performance functions with barrier Lyapunov functions guarantees that attitude-tracking errors converge to predefined ranges within a bounded time. The proposed method ensures that all signals in the closed-loop system are practically fixed-time stable, with attitude tracking errors constrained within predetermined boundaries and Zeno behavior effectively excluded. Lastly, case studies are conducted to demonstrate the effectiveness and practical applicability of the developed methodology.

针对受执行器故障、参数不确定性和环境干扰影响的刚性航天器姿态跟踪问题，提出了一种基于定时扰动观测器（DO）的动态事件触发容错控制（DETFTC）框架。与依赖于初始条件或上扰动界的传统DO方法相比，这项工作引入了一个固定时间DO来准确地重建由外源干扰、惯性不确定性和执行器故障引起的集总扰动。进一步开发了DETFTC策略，以尽量减少不必要的控制信号更新，同时确保快速和精确的姿态跟踪。将固定时间性能函数与屏障Lyapunov函数集成，保证姿态跟踪误差在有限时间内收敛到预定义范围。该方法保证了闭环系统中所有信号实际上是定时稳定的，姿态跟踪误差被约束在预定边界内，并有效地排除了芝诺行为。最后，通过案例分析证明了所开发方法的有效性和实际适用性。

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引用次数: 0

LSTM-empowered reinforcement learning in Bi-level optimal control for nonlinear systems with uncertain dynamics 基于lstm的非线性系统双级最优控制强化学习。

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.2025.11.027

Roya Khalili Amirabadi , Mohsen Jalaeian-Farimani , Omid S. Fard

This paper introduces a bi-level optimization framework for the optimal control of nonlinear continuous-time systems with uncertain dynamics, seamlessly integrating Long Short-Term Memory (LSTM) networks with an actor-critic reinforcement learning (RL) architecture. By synergizing Hamiltonian-based optimal control with online uncertainty estimation, the proposed method achieves robust trajectory tracking without reliance on offline training. The master level optimizes control policies using an HJB-inspired formulation, while the slave level employs LSTM networks to dynamically estimate lumped uncertainties, ensuring adaptability to time-varying disturbances. Rigorous stability analysis establishes uniform ultimate boundedness of the tracking error, guaranteeing robust performance. Extensive simulations on a skid-steering tracked robot across diverse trajectories demonstrate the framework’s superior tracking precision, energy efficiency, and disturbance rejection compared to conventional adaptive control and model-based virtual reference trajectory schemes. This computationally efficient and theoretically grounded approach offers a scalable solution for autonomous systems operating in uncertain environments, advancing the paradigm of RL-based optimal control.

本文介绍了一种双级优化框架，用于具有不确定动态的非线性连续系统的最优控制，无缝地将长短期记忆（LSTM）网络与actor-critic强化学习（RL）架构集成在一起。该方法将基于哈密顿的最优控制与在线不确定性估计相结合，实现了不依赖离线训练的鲁棒轨迹跟踪。主级使用受hlb启发的公式优化控制策略，而从级使用LSTM网络动态估计集总不确定性，确保对时变干扰的适应性。严格的稳定性分析建立了跟踪误差一致的最终有界性，保证了系统的鲁棒性。对滑移转向履带式机器人在不同轨迹上的广泛仿真表明，与传统的自适应控制和基于模型的虚拟参考轨迹方案相比，该框架具有优越的跟踪精度、能量效率和抗干扰性。这种计算效率高、理论基础扎实的方法为在不确定环境中运行的自主系统提供了可扩展的解决方案，推进了基于强化学习的最优控制范式。

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引用次数: 0

Robust composite event-triggered control for heterogeneous ships: Application to an auto-detecting operation 异构船舶的鲁棒复合事件触发控制：在自动检测操作中的应用。

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.2025.11.035

Guoqing Zhang , Qiong Cao , Jiqiang Li , Chenguang Liu , Weidong Zhang

In this paper, a composite event-triggered control algorithm based on hysteresis quantizer is proposed, which not only effectively controls the position and speed consistency of the real ship and the virtual ship, but also effectively reduces the occupation of communication resources within the ships. By the improved L2 virtual ship (L2-VS) guidance method combining leader-following guidance principle and artificial potential field (APF), the special guidance task requirement of that heterogeneous ship formation detecting trespass ship with time-varying formation is satisfied. Furthermore, to address system model uncertainties, neural network approximation is employed, while supplementary adaptive compensation mechanisms effectively mitigate adverse effects arising from quantization processes and parametric uncertainties. On the basis of Lyapunov analysis, all the signals of the closed-loop control system are guaranteed to be semi-globally uniformly ultimately bounded (SGUUB). Finally, the algorithm is validated in the specific maritime application of detection and reformation for a heterogeneous ship formation and the results demonstrate its effectiveness.

本文提出了一种基于迟滞量化器的复合事件触发控制算法，该算法不仅有效地控制了实船和虚拟船的位置和速度一致性，而且有效地减少了对船舶内部通信资源的占用。通过将先导-跟随制导原理与人工势场（APF）相结合的改进L2虚拟船（L2- vs）制导方法，满足了时变编队异构舰艇编队探测越界舰艇的特殊制导任务要求。此外，为了解决系统模型的不确定性，采用了神经网络逼近，同时补充的自适应补偿机制有效地减轻了量化过程和参数不确定性带来的不利影响。在Lyapunov分析的基础上，保证了闭环控制系统的所有信号是半全局一致最终有界的。最后，将该算法应用于异构舰艇编队的检测与改造，验证了算法的有效性。

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引用次数: 0

Mutual calibration estimation of dynamic states and static parameters for skateboard chassis vehicle 滑板底盘车辆动静态参数互标定估计。

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.2025.11.040

Zequn Bei, Xiang Chen, Guan Zhou, Xu Wang, Wanzhong Zhao, Kailong Hou

The non-fixed coupling between skateboard chassis and superstructures leads to significant challenges in accurate vehicle dynamic state estimation. To address this issue, this paper proposes a mutual calibration estimation method. First, the nonlinear 7-DOF vehicle dynamic model is established as the foundation for the mutual calibration framework. Then, a novel calibrated forgetting factor recursive least squares (C-FFRLS) estimator is developed within the dynamic-static parameters mutual calibration framework, which can estimate vehicle mass and center of gravity (CoG) position while computing the yaw moment of inertia via the parallel axis theorem. The estimated static parameters calibrate both the model parameters and the noise characteristics of the dynamic observer. Finally, a calibrated adaptive robust Cubature Kalman filter (C-ARCKF) is proposed to estimate vehicle states; the dynamic observation results are used to calibrate the static observer, thereby establishing a closed-loop mutual calibration system. The simulation and experimental results validate the effectiveness of the proposed method.

滑板底盘与上部结构之间的非固定耦合给车辆动态状态的准确估计带来了巨大的挑战。为了解决这一问题，本文提出了一种互校正估计方法。首先，建立了非线性七自由度车辆动力学模型，作为互标定框架的基础；然后，在动静参数互标定框架下，提出了一种校正后的遗忘因子递推最小二乘（C-FFRLS）估计器，该估计器可以在估计车辆质量和重心位置的同时，根据平行轴定理计算横摆惯量。估计的静态参数校准了模型参数和动态观测器的噪声特性。最后，提出了一种校正后的自适应鲁棒Cubature卡尔曼滤波器（C-ARCKF）来估计车辆状态；利用动态观测结果对静态观测器进行标定，从而建立闭环互标定系统。仿真和实验结果验证了该方法的有效性。

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引用次数: 0

Constructive design of disturbance observer-based safe control for strict-feedback nonlinear systems with disturbances 具有扰动的严格反馈非线性系统基于扰动观测器的安全控制构造设计。

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.2025.11.041

Zhong-Qiu Chen , Jie Tao , Ming Lin , Chun-Yi Su , Renquan Lu , Yong-Hua Liu

Existing safe backstepping control methods often rely on precise system models, which limits their robustness against external disturbances frequently encountered in real-world applications. To address the problem of safe control for strict-feedback nonlinear systems subject to unknown disturbances, this paper proposes a novel constructive control framework that integrates a disturbance observer, filtered backstepping, and control barrier functions (CBFs). A disturbance observer is first developed to estimate and compensate for unknown disturbances, providing a less conservative alternative to conventional robust methods that rely on “worst-case” assumptions. The estimated disturbance is then embedded into a recursive design framework that combines filtered backstepping with CBFs to construct smooth virtual and actual controllers. This unified approach mitigates the “explosion of complexity” typically associated with safe backstepping while avoiding the nonsmoothness inherent in optimization-based CBF controllers. Finally, the effectiveness of the proposed method is validated through numerical simulations and real-time experiments on a Franka Emika Panda robotic arm.

现有的安全反演控制方法通常依赖于精确的系统模型，这限制了它们在实际应用中对外部干扰的鲁棒性。为了解决受未知扰动的严格反馈非线性系统的安全控制问题，本文提出了一种新的构造控制框架，该框架集成了扰动观测器、滤波反演和控制屏障函数（cbf）。干扰观测器首先用于估计和补偿未知干扰，为依赖“最坏情况”假设的传统鲁棒方法提供了一种不太保守的替代方法。然后将估计的干扰嵌入到一个递归设计框架中，该框架结合滤波反演和cbf来构建平滑的虚拟和实际控制器。这种统一的方法减轻了通常与安全回溯相关的“复杂性爆炸”，同时避免了基于优化的CBF控制器固有的非平滑性。最后，在Franka Emika Panda机械臂上进行了数值模拟和实时实验，验证了所提方法的有效性。

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引用次数: 0

Potential impacts of delay on pinning impulsive secure synchronization control of delayed networks 延迟对延迟网络固定脉冲安全同步控制的潜在影响。

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.2025.11.049

Lingzhong Zhang , Jianquan Lu , Qihua Ruan , Jungang Lou

This paper explores the impact of delays on the secure and anti-disturbance synchronization of time-delayed networks under a pinning impulsive control strategy. An exponential-type Lyapunov function and a novel impulsive delay inequality are proposed, extracting and incorporating the information of time delay existing in continuous dynamics into Lyapunov-based conditions. A probabilistic channel failure model is used to analyze the impact of deception attacks and disturbances. Sufficient conditions for mean square bounded synchronization (MSBS) are derived, showing that appropriate delays might mitigate attacks and accelerate convergence. The minimum pinning control threshold to achieve MSBS is derived from the attacks. The factors affecting error bounds are discussed. Numerical simulations on small-world networks, multi-agent systems, and Chua’s circuit validate the theory.

研究了在钉住脉冲控制策略下，时延对时滞网络安全与抗干扰同步的影响。提出了一种指数型Lyapunov函数和一种新的脉冲时滞不等式，将连续动力学中存在的时滞信息提取并结合到基于Lyapunov的条件中。采用概率信道失效模型分析欺骗攻击和干扰对信道的影响。推导了均方有界同步（MSBS）的充分条件，表明适当的延迟可以减轻攻击并加速收敛。实现MSBS的最小绑定控制阈值来源于攻击。讨论了影响误差范围的因素。在小世界网络、多智能体系统和Chua电路上的数值模拟验证了该理论。

引用次数: 0