Journal of The Franklin Institute-engineering and Applied Mathematics最新文献

英文中文

Intelligent disturbance rejection control for precision operations in space missions 空间任务精密操作的智能抗扰控制

IF 4.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2026-01-12 DOI: 10.1016/j.jfranklin.2025.108373

Xumeng Cheng , Jia Ma , Congxi Liu , Gangfeng Liu , Meng Chen , Chongfeng Zhang , Jie Zhao

This paper proposes an intelligent compound disturbance rejection control framework integrating a novel Unknown System Dynamics Estimator (USDE) with Extreme Learning Machine (ELM). The USDE reconstructs the lumped term encompassing system parametric uncertainties and external disturbances online, requiring only real-time measurements of joint positions, velocities, and input torques, thereby eliminating dependency on a precise dynamic model. The framework further incorporates an ELM neural network to construct a disturbance rejection controller with direct joint torque actuation. Under randomly initialized ELM input weights, this architecture achieves effective prediction and compensation of acceleration errors through dynamic optimization of the output weights. Based on Lyapunov stability theory, the global stability of both the closed-loop tracking error and the USDE estimation error is rigorously proven. Simulations and experiments on a Franka Emika Panda robot demonstrate that the proposed method maintains high-precision trajectory tracking performance under simulated space disturbance scenarios, including unknown dynamic model mismatch, gravity variations, and sudden external disturbances. This work provides a theoretical framework and a universal implementation scheme, independent of precise dynamic models, for solving the challenge of fine manipulation control in harsh, unknown environments for open-space robotic systems.

提出了一种将未知系统动力学估计器（USDE）与极限学习机（ELM）相结合的智能复合抗扰控制框架。USDE在线重建包含系统参数不确定性和外部干扰的集总项，只需要实时测量关节位置、速度和输入扭矩，从而消除了对精确动态模型的依赖。该框架进一步结合ELM神经网络构建了直接关节力矩驱动的抗扰控制器。在随机初始化ELM输入权值的情况下，通过动态优化输出权值，实现对加速度误差的有效预测和补偿。基于Lyapunov稳定性理论，严格证明了闭环跟踪误差和USDE估计误差的全局稳定性。在Franka Emika Panda机器人上的仿真和实验表明，该方法在模拟的空间干扰场景下保持了高精度的轨迹跟踪性能，包括未知动态模型失配、重力变化和突发性外部干扰。这项工作提供了一个独立于精确动态模型的理论框架和通用实施方案，用于解决开放空间机器人系统在恶劣未知环境下的精细操作控制挑战。

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引用次数: 0

Fault Detection of Unmanned Surface Vehicles Based on the Combination of Supervised and Unsupervised Models 基于监督与无监督模型相结合的无人水面车辆故障检测

IF 4.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2026-01-11 DOI: 10.1016/j.jfranklin.2026.108404

Chen Zhou , Hui Ye , Yizhen Meng , Xin Tian , Yang Tao

This study proposes an innovative fault detection method for unmanned surface vehicles (USVs), integrating supervised and unsupervised learning to significantly enhance the fault detection rate (FDR) and reduce the false alarm rate (FAR). The core innovation lies in designing a reversible bridging network that efficiently fuses the residual features of unsupervised and supervised models.

By analyzing multimodal fault characteristics, both unsupervised and supervised neural network models are constructed. The unsupervised model generates residual signals by minimizing reconstruction errors, while the supervised model produces feature residual signals by optimizing the loss function. The reversible bridging network merges the two types of residual features, significantly improving detection accuracy and robustness.

Simulation experiments demonstrate that the hybrid model achieves a fault detection rate of 94.65%, far exceeding the performance of using only unsupervised or supervised models, with a false alarm rate of only 1.15%. This method provides a new technical approach for USV fault diagnosis in complex scenarios, holding significant theoretical and practical application value.

本文提出了一种创新的无人水面车辆故障检测方法，将监督学习与无监督学习相结合，显著提高了故障检测率（FDR），降低了误报率（FAR）。其核心创新在于设计了一个可逆桥接网络，有效地融合了无监督模型和有监督模型的残差特征。通过分析多模态故障特征，分别构建了无监督和有监督神经网络模型。无监督模型通过最小化重构误差产生残差信号，有监督模型通过优化损失函数产生特征残差信号。可逆桥接网络融合了两类残差特征，显著提高了检测精度和鲁棒性。仿真实验表明，混合模型的故障检测率达到94.65%，远远超过单纯使用无监督或有监督模型的性能，虚警率仅为1.15%。该方法为USV复杂场景下的故障诊断提供了新的技术途径，具有重要的理论和实际应用价值。

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引用次数: 0

Interval type-2 fuzzy model predictive control for CPS with dynamic event-based scheduling protocol and actuator failure 基于动态事件调度协议和执行器故障的CPS区间2型模糊模型预测控制

IF 4.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2026-01-11 DOI: 10.1016/j.jfranklin.2026.108413

Cancan Wang , Fucai Liu , Lining Fu , Shuang Ju

This paper presents an interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy model predictive control (MPC) strategy for a nonlinear cyber-physical system (CPS) subject to dynamic event-based scheduling protocol and actuator failure. In contrast to type-1 fuzzy model, IT2 T-S fuzzy model with lower and upper membership functions can capture the uncertain parameters of system. To save network resources and avoid data collision problems, two dynamic event-based scheduling protocols are proposed in the fuzzy MPC algorithm. Compared with existing protocols, the scheduling protocols can simultaneously adjust whether to release the sampling instant and which node to transmit. Moreover, inevitable actuator failure issue is addressed by establishing a failure model. Furthermore, a state observer is off-line designed to reduce the calculation burden and the model predictive controller gains are on-line solved to stabilize the CPS. Finally, simulation results show that the triggered rates of dynamic event-based scheduling protocols (28.33% and 26.67% in Example 1) are lower than those of static event-based scheduling protocols (63.33% and 61.67% in Example 1), indicating the validity of proposed method.

针对基于动态事件调度协议和执行器失效的非线性网络物理系统，提出了区间型2 Takagi-Sugeno （IT2 T-S）模糊模型预测控制策略。与第一类模糊模型相比，具有上下隶属函数的IT2 T-S模糊模型能够捕捉系统的不确定参数。为了节省网络资源和避免数据冲突问题，在模糊MPC算法中提出了两种基于事件的动态调度协议。与现有协议相比，调度协议可以同时调整是否释放采样时间和传输哪个节点。通过建立故障模型，解决了执行机构不可避免的故障问题。此外，离线设计状态观测器以减少计算负担，在线求解模型预测控制器增益以稳定CPS。仿真结果表明，动态事件调度协议的触发率（例1中分别为28.33%和26.67%）低于静态事件调度协议的触发率（例1中分别为63.33%和61.67%），验证了所提方法的有效性。

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引用次数: 0

Fast data-driven iterative learning control for linear system with output disturbance 具有输出扰动的线性系统的快速数据驱动迭代学习控制

IF 4.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2026-01-11 DOI: 10.1016/j.jfranklin.2026.108414

Jia Wang , Leander Hemelhof , Ivan Markovsky , Panagiotis Patrinos

This paper studies data-driven iterative learning control (ILC) for linear time-invariant (LTI) systems with unknown dynamics, output disturbances and input box-constraints. Our main contributions are: 1) using a non-parametric data-driven representation of the system dynamics, for dealing with the unknown system dynamics in the context of ILC, 2) design of a fast ILC method for dealing with output disturbances, model uncertainty and input constraints. A complete design method is given in this paper, which consists of the data-driven representation, controller formulation, acceleration strategy and convergence analysis. A batch of numerical experiments and a case study on a high-precision robotic motion system are given in the end to show the effectiveness of the proposed method.

研究了具有未知动态、输出扰动和输入框约束的线性时不变系统的数据驱动迭代学习控制。我们的主要贡献是：1)使用非参数数据驱动的系统动力学表示，用于处理ILC背景下的未知系统动力学；2)设计了一种快速的ILC方法，用于处理输出干扰、模型不确定性和输入约束。本文给出了一种完整的设计方法，包括数据驱动表示、控制器制定、加速策略和收敛分析。最后以高精度机器人运动系统为例进行了数值实验，验证了该方法的有效性。

引用次数: 0

Optimizing bioinspired neurodynamic formation control for traffic cone robots under control input constraints: A noncooperative game approach 控制输入约束下交通锥机器人的仿生神经动力学编队控制优化：一种非合作博弈方法

IF 4.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2026-01-10 DOI: 10.1016/j.jfranklin.2025.108383

Jiale Zhang , Dongsheng Zhang , Zhiyong Li , Shengjie Jiao , Chuanwei Zhang , Siyuan Chang , Meng Wei

This study presents an optimized bioinspired neurodynamic control framework for the formation placement and recovery control of traffic cone robots (TCRs) under constrained control inputs. A dynamic error model is constructed based on positional states, upon which a backstepping controller is designed and integrated with a bioinspired neurodynamic module to mitigate infeasible control commands arising from large initial deviations, ensuring actuator feasibility. Lyapunov-based analysis demonstrates closed-loop stability and guarantees the asymptotic convergence of formation errors. In addition, a multi-parameter optimization framework grounded in noncooperative game theory is proposed to identify optimal control gains at the Nash equilibrium, minimizing a predefined performance cost. The effectiveness, robustness, and practical applicability of the approach are validated through both numerical simulations and physical experiments, demonstrating its potential for real-world TCR formation operations.

本文提出了一种优化的生物神经动力学控制框架，用于约束控制输入条件下交通锥机器人的队形放置和恢复控制。建立了基于位置状态的动态误差模型，在此基础上设计了后退控制器，并与生物神经动力学模块集成，以减轻由于初始偏差过大而导致的控制命令不可行，保证了执行器的可行性。基于lyapunov的分析证明了闭环稳定性，并保证了编队误差的渐近收敛。此外，提出了一个基于非合作博弈论的多参数优化框架，以确定纳什均衡下的最优控制收益，使预定义的性能成本最小化。通过数值模拟和物理实验验证了该方法的有效性、鲁棒性和实用性，证明了其在实际TCR地层作业中的潜力。

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引用次数: 0

Recursive H∞ filtering: Computing gain using LMI for backward Euler method-based disturbed models 递归H∞滤波：基于后向欧拉方法的扰动模型的LMI增益计算

IF 4.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2026-01-09 DOI: 10.1016/j.jfranklin.2026.108406

José A. Andrade-Lucio, Oscar G. Ibarra-Manzano, Miguel A. Vázquez-Olguín, Yuriy S. Shmaliy

Robust H_∞ filtering has been developed using the transfer function approach to provide estimates with guaranteed energy-to-energy performance. In this paper, we use a previously proven bounded real lemma corresponding to the backward Euler method-based disturbed models and show how to numerically compute the bias correction gain K for the recursive H_∞ filter, which is uniquely responsible for its performance. The unknown disturbance is viewed as a Gauss-Markov sequence with an uncertain coloredness factor. Since the error covariance is a quadratic function of K, two theorems are proved and two algorithms are developed to compute K using a linear matrix inequality. A comparison of the H_∞, Kalman, and unbiased finite impulse response (UFIR) filters is provided in terms of mean square error, robustness, and estimation quality. It is shown numerically and experimentally that the gain K of the H_∞ filter is between the Kalman gain and the UFIR filter gain, and that under certain conditions the H_∞ filter can outperform both of them.

鲁棒H∞滤波已开发使用传递函数方法提供估计保证能量对能量的性能。在本文中，我们使用先前证明的有界实引理对应于基于后向欧拉方法的扰动模型，并展示了如何数值计算递推H∞滤波器的偏差校正增益K，这是其性能的唯一原因。将未知扰动视为具有不确定颜色因子的高斯-马尔可夫序列。由于误差协方差是K的二次函数，因此证明了两个定理，并开发了两个算法来使用线性矩阵不等式计算K。从均方误差、鲁棒性和估计质量方面比较了H∞、卡尔曼和无偏有限脉冲响应（UFIR）滤波器。数值和实验表明，H∞滤波器的增益K介于卡尔曼增益和UFIR滤波器增益之间，在一定条件下，H∞滤波器的性能优于两者。

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引用次数: 0

Resilient adaptive NN-based distributed consensus for nonlinear MASs subject to DoS attacks and uncertainties 受DoS攻击和不确定性影响的非线性质量的基于弹性自适应神经网络的分布式一致性

IF 4.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2026-01-09 DOI: 10.1016/j.jfranklin.2026.108401

Shengli Du , Qiong Wu , Honggui Han , Junfei Qiao

This paper investigates the fully distributed consensus control problem for nonlinear multiagent systems (MASs) subject to denial-of-service (DoS) attacks, external disturbances, and unmodeled nonlinearities. To mitigate the adverse effects of such uncertainties, a radial basis function neural network (RBFNN)-based adaptive control law is developed, combined with sign-function-based update rules to ensure robust approximation and compensation. In addressing the communication constraints induced by DoS attacks, a dynamic event-triggered switching control strategy is further proposed to reduce communication load while maintaining resilience against intermittent network failures. To eliminate the reliance on any global information, a fully distributed implementation is achieved, enhancing the scalability and practicality of the control scheme. With the assistance of Lyapunov stability theory, some bounded consensus conditions have been established. Finally, two simulation studies are conducted to demonstrate the effectiveness and robustness of the proposed control approach.

研究了具有拒绝服务（DoS）攻击、外部干扰和未建模非线性的非线性多智能体系统（MASs）的完全分布式共识控制问题。为了减轻这种不确定性的不利影响，提出了一种基于径向基函数神经网络（RBFNN）的自适应控制律，并结合基于符号函数的更新规则来保证鲁棒逼近和补偿。为了解决DoS攻击导致的通信约束问题，进一步提出了一种动态事件触发切换控制策略，在降低通信负荷的同时保持对间歇性网络故障的弹性。为了消除对全局信息的依赖，实现了全分布式实现，增强了控制方案的可扩展性和实用性。利用Lyapunov稳定性理论，建立了一些有界一致条件。最后，进行了两个仿真研究，验证了所提控制方法的有效性和鲁棒性。

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引用次数: 0

Extended state observer-based adaptive iterative learning control of redundant manipulators subject to dual-domain disturbances 基于扩展状态观测器的冗余机械臂双域扰动自适应迭代学习控制

IF 4.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2026-01-09 DOI: 10.1016/j.jfranklin.2026.108415

Jiayu Zhao, Tao Zhao, Hainan Yang

Redundant manipulators are widely employed in complex and precision-critical tasks, yet achieving effective disturbance rejection remains challenging due to their highly nonlinear dynamics, strong inter-joint coupling, and susceptibility to both time- and batch-varying uncertainty disturbances. Existing iterative learning control approaches often struggle to cope with such disturbances, especially when these disturbances vary within each iteration cycle, which limits their applicability in repetitive high-accuracy tasks. To address this gap, this paper proposes an adaptive iterative learning control (AILC) scheme integrated with an interval type-2 fuzzy extended state observer (IT2FESO) to enhance both tracking accuracy and disturbance rejection performance. First, an interval type-2 fuzzy model of the manipulator is constructed via the fuzzy c-regression clustering algorithm to capture inherent nonlinearities and model uncertainties. Then, the IT2FESO is designed to estimate time- and batch-varying disturbances in real time, and its output is incorporated into the AILC to enable autonomous parameter adaptation and accurate target trajectory tracking. Finally, a compound energy function is formulated to rigorously establish the convergence conditions of the tracking errors. Simulation studies on a redundant manipulator demonstrate that the proposed approach achieves superior tracking accuracy and disturbance rejection performance under time- and batch-varying uncertainty disturbances.

冗余机械手广泛应用于复杂和精度要求高的任务中，但由于其高度非线性动力学、强关节间耦合以及对时间和批量变化的不确定性干扰的敏感性，实现有效的抗干扰仍然具有挑战性。现有的迭代学习控制方法往往难以处理这些干扰，特别是当这些干扰在每个迭代周期内变化时，这限制了它们在重复的高精度任务中的适用性。为了解决这一问题，本文提出了一种结合区间2型模糊扩展状态观测器（IT2FESO）的自适应迭代学习控制（AILC）方案，以提高跟踪精度和抗扰性能。首先，利用模糊c回归聚类算法建立了机械臂的区间2型模糊模型，以捕捉其固有的非线性和模型的不确定性；然后，设计了IT2FESO来实时估计时变和批变干扰，并将其输出纳入AILC，以实现自主参数自适应和精确的目标轨迹跟踪。最后，构造了复合能量函数，严格建立了跟踪误差的收敛条件。对冗余度机械臂的仿真研究表明，该方法在时变和批变不确定性干扰下具有良好的跟踪精度和抗扰性能。

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引用次数: 0

Distributed aggregative optimization for nonlinear multi-agent systems with state delays under time-varying graphs using sampling technology 时变图下具有状态延迟的非线性多智能体系统的分布聚合优化

IF 4.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2026-01-08 DOI: 10.1016/j.jfranklin.2026.108403

Cong Li , Qingling Wang

This paper investigates the distributed aggregative optimization (DAO) problem for high-order nonlinear multi-agent systems over time-varying graphs. We first introduce a new class of aggregative regulation variables that integrate sampled neighbor data during each sampling period, these variables are upgraded through an auxiliary function. By leveraging the C-step consensus contraction method alongside these variables, we reformulate the time-varying graphs DAO problem as a regulation problem, thereby facilitating the use of classical control techniques to address complex nonlinear dynamics. Additionally, we propose a control law that incorporates performance functions and aggregative regulation variables to solve the DAO problem for high-order nonlinear agents with state delays. Numerical simulations demonstrate the validity of the proposed framework.

研究了具有时变图的高阶非线性多智能体系统的分布式聚合优化问题。我们首先引入了一类新的聚合调节变量，这些变量在每个采样周期内集成了采样的邻居数据，这些变量通过辅助函数进行升级。通过利用c步共识收缩方法以及这些变量，我们将时变图DAO问题重新表述为调节问题，从而促进使用经典控制技术来解决复杂的非线性动力学。此外，我们还提出了一种结合性能函数和聚合调节变量的控制律来解决具有状态延迟的高阶非线性智能体的DAO问题。数值仿真验证了该框架的有效性。

引用次数: 0

Double observers-based node and link synchronization of fuzzy Markovian jump positive complex networks 基于双观测器的模糊马尔可夫跳正复杂网络节点与链路同步

IF 4.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2026-01-08 DOI: 10.1016/j.jfranklin.2025.108391

Shouting Hong , Haoyue Yang , Tarek Raïssi , Junfeng Zhang

This paper investigates the dual-synchronization problem of fuzzy positive Markovian jump complex networks with dynamic links based on double observations. A class of fuzzy positive Markovian jump complex networks is established by introducing dynamic links between nodes. Subsequently, a controller and the corresponding link coupling term are designed to achieve the positive and synchronous of the node systems and the link systems. Then, a synchronization strategy is proposed based on the observations of node and link states. The main contributions are as follows: (i) A dual-synchronization framework is constructed for both nodes and links by designing the controller and the coupling term, (ii) A synchronization control strategy is proposed based on double observers of node subsystems and link subsystems, and (iii) A manageable approach is developed for design and analysis by employing linear programming and constructing co-positive Lyapunov functions. Finally, the effectiveness and feasibility of the proposed approaches are illustrated via simulation examples.

研究了基于双观测的动态链路模糊正马尔可夫跳变复杂网络的双同步问题。通过引入节点间的动态连接，建立了一类模糊正马尔可夫跳变复杂网络。然后设计控制器和相应的链路耦合项，实现节点系统与链路系统的正同步。然后，提出了一种基于节点和链路状态观测的同步策略。主要贡献如下：(i)通过设计控制器和耦合项，构建了节点和链路的双同步框架；（ii）提出了基于节点子系统和链路子系统双观测器的同步控制策略；（iii）采用线性规划和构造共正Lyapunov函数，开发了一种可管理的设计和分析方法。最后，通过仿真算例验证了所提方法的有效性和可行性。

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