Pub Date : 2026-01-13DOI: 10.1016/j.jfranklin.2026.108418
Lin Xiao, Yuyang Liu, Qiuyue Zuo, Mengrui Cao, Wangqiu Kuang
Sliding mode control has been extensively applied in unmanned aerial vehicle control for its robustness to nonlinear systems. However, its performance is sensitive to initial conditions and controller parameters, particularly reaching time. To address this, this paper proposes a Fuzzy Weighted Self-Allocation Sliding Mode Controller (FWSASMC) for quadrotor UAV trajectory tracking under bounded disturbances. By leveraging zeroing neural dynamics, the FWSASMC achieves fixed-time convergence through a fuzzy-weighted self-allocation scheme and an adaptive parameter. The scheme identifies varying effects of components in the conventional activation function during neural dynamics and uses fuzzy-optimized weights to coordinate them, accelerating convergence. The improved activation function is smoothed to construct a non-singular sliding surface, while the adaptive parameter refines the reaching law. Theoretical analysis and simulations verify convergence, demonstrating superior trajectory tracking performance and highlighting its potential for UAV applications, particularly in time-varying target tracking.
{"title":"A fuzzy weighted self-allocation sliding mode controller for UAV trajectory tracking","authors":"Lin Xiao, Yuyang Liu, Qiuyue Zuo, Mengrui Cao, Wangqiu Kuang","doi":"10.1016/j.jfranklin.2026.108418","DOIUrl":"10.1016/j.jfranklin.2026.108418","url":null,"abstract":"<div><div>Sliding mode control has been extensively applied in unmanned aerial vehicle control for its robustness to nonlinear systems. However, its performance is sensitive to initial conditions and controller parameters, particularly reaching time. To address this, this paper proposes a Fuzzy Weighted Self-Allocation Sliding Mode Controller (FWSASMC) for quadrotor UAV trajectory tracking under bounded disturbances. By leveraging zeroing neural dynamics, the FWSASMC achieves fixed-time convergence through a fuzzy-weighted self-allocation scheme and an adaptive parameter. The scheme identifies varying effects of components in the conventional activation function during neural dynamics and uses fuzzy-optimized weights to coordinate them, accelerating convergence. The improved activation function is smoothed to construct a non-singular sliding surface, while the adaptive parameter refines the reaching law. Theoretical analysis and simulations verify convergence, demonstrating superior trajectory tracking performance and highlighting its potential for UAV applications, particularly in time-varying target tracking.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 4","pages":"Article 108418"},"PeriodicalIF":4.2,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145969349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 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.
{"title":"Intelligent disturbance rejection control for precision operations in space missions","authors":"Xumeng Cheng , Jia Ma , Congxi Liu , Gangfeng Liu , Meng Chen , Chongfeng Zhang , Jie Zhao","doi":"10.1016/j.jfranklin.2025.108373","DOIUrl":"10.1016/j.jfranklin.2025.108373","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 4","pages":"Article 108373"},"PeriodicalIF":4.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-11DOI: 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.
{"title":"Fault Detection of Unmanned Surface Vehicles Based on the Combination of Supervised and Unsupervised Models","authors":"Chen Zhou , Hui Ye , Yizhen Meng , Xin Tian , Yang Tao","doi":"10.1016/j.jfranklin.2026.108404","DOIUrl":"10.1016/j.jfranklin.2026.108404","url":null,"abstract":"<div><div>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.</div><div>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.</div><div>Simulation experiments demonstrate that the <strong>hybrid model</strong> 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.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 3","pages":"Article 108404"},"PeriodicalIF":4.2,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-11DOI: 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.
{"title":"Interval type-2 fuzzy model predictive control for CPS with dynamic event-based scheduling protocol and actuator failure","authors":"Cancan Wang , Fucai Liu , Lining Fu , Shuang Ju","doi":"10.1016/j.jfranklin.2026.108413","DOIUrl":"10.1016/j.jfranklin.2026.108413","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 4","pages":"Article 108413"},"PeriodicalIF":4.2,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-11DOI: 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.
{"title":"Fast data-driven iterative learning control for linear system with output disturbance","authors":"Jia Wang , Leander Hemelhof , Ivan Markovsky , Panagiotis Patrinos","doi":"10.1016/j.jfranklin.2026.108414","DOIUrl":"10.1016/j.jfranklin.2026.108414","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 3","pages":"Article 108414"},"PeriodicalIF":4.2,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Optimizing bioinspired neurodynamic formation control for traffic cone robots under control input constraints: A noncooperative game approach","authors":"Jiale Zhang , Dongsheng Zhang , Zhiyong Li , Shengjie Jiao , Chuanwei Zhang , Siyuan Chang , Meng Wei","doi":"10.1016/j.jfranklin.2025.108383","DOIUrl":"10.1016/j.jfranklin.2025.108383","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 3","pages":"Article 108383"},"PeriodicalIF":4.2,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-09DOI: 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.
{"title":"Recursive H∞ filtering: Computing gain using LMI for backward Euler method-based disturbed models","authors":"José A. Andrade-Lucio, Oscar G. Ibarra-Manzano, Miguel A. Vázquez-Olguín, Yuriy S. Shmaliy","doi":"10.1016/j.jfranklin.2026.108406","DOIUrl":"10.1016/j.jfranklin.2026.108406","url":null,"abstract":"<div><div>Robust <em>H</em><sub>∞</sub> filtering has been developed using the transfer function approach to provide estimates with guaranteed <em>energy-to-energy</em> 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 <strong>K</strong> for the recursive <em>H</em><sub>∞</sub> 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 <strong>K</strong>, two theorems are proved and two algorithms are developed to compute <strong>K</strong> using a linear matrix inequality. A comparison of the <em>H</em><sub>∞</sub>, 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 <strong>K</strong> of the <em>H</em><sub>∞</sub> filter is between the Kalman gain and the UFIR filter gain, and that under certain conditions the <em>H</em><sub>∞</sub> filter can outperform both of them.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 3","pages":"Article 108406"},"PeriodicalIF":4.2,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-09DOI: 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.
{"title":"Resilient adaptive NN-based distributed consensus for nonlinear MASs subject to DoS attacks and uncertainties","authors":"Shengli Du , Qiong Wu , Honggui Han , Junfei Qiao","doi":"10.1016/j.jfranklin.2026.108401","DOIUrl":"10.1016/j.jfranklin.2026.108401","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 3","pages":"Article 108401"},"PeriodicalIF":4.2,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-09DOI: 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.
{"title":"Extended state observer-based adaptive iterative learning control of redundant manipulators subject to dual-domain disturbances","authors":"Jiayu Zhao, Tao Zhao, Hainan Yang","doi":"10.1016/j.jfranklin.2026.108415","DOIUrl":"10.1016/j.jfranklin.2026.108415","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 3","pages":"Article 108415"},"PeriodicalIF":4.2,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 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.
{"title":"Distributed aggregative optimization for nonlinear multi-agent systems with state delays under time-varying graphs using sampling technology","authors":"Cong Li , Qingling Wang","doi":"10.1016/j.jfranklin.2026.108403","DOIUrl":"10.1016/j.jfranklin.2026.108403","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 3","pages":"Article 108403"},"PeriodicalIF":4.2,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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