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Adaptive torque estimation-based nonlinear H∞ control of modular robot manipulators with uncertain environments

Neural Computing and Applications 2025年第3期37卷 1617-1631页

作者： Dong, Bo Wang, Yuge An, Tianjiao Cui, Yiming Zhu, Xinye Department of Control Science and Engineering Changchun University of Technology Changchun130012 China

Modular robot manipulators (MRMs) based on harmonic drive (HD) transmissions perform various target tasks in unknown environments, and the main challenge is to overcome the uncertain noise and controller errors of systems generated by device vibrations while facing with unknown external disturbance. To address these challenges, a variational Bayesian (VB)-based extended Kalman filter (VBEKF) is developed as part of the MRM dynamic model to mitigate bias in torque estimation resulting from external interference within HD model joints. Furthermore, leveraging a two-player zero-sum game strategy, a robust adaptive dynamic programming (ADP) method based on single critic neural network (NN) is formulated to solve the Hamilton–Jacobi–Isaacs (HJI) equation in H∞ control problem, ultimately yielding an approximate optimal solution for H∞ control. The Lyapunov theory guarantees that the trajectory tracking error of a closed-loop MRM system remains ultimately uniformly bounded (UUB), even in the presence of unknown environmental disturbances. Ultimately, the experiment results are given to illustrate the advantages and effectiveness of the proposed method. The experimental results show that the proposed method reduces the control torque error by ∼20% compared with the existing control methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Extended Kalman filters

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Dynamic parameter identification of modular robot manipulators based on hybrid optimization strategy: genetic algorithm and least squares method

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Soft Computing 2024年第17-18期28卷 9991-10005页

作者： Lu, Zengpeng Wei, Chengyu Ni, Daiwei Bi, Jiabin Wang, Qingyun Li, Yan Department of Control Science and Engineering Changchun University of Technology Changchun130012 China Department of Mechanical Engineering Changchun University of Technology Changchun130012 China

Uncertainty in robot dynamic systems is caused by model errors in the dynamic parameters, and accurate identification of the dynamic parameters is essential to improve the control accuracy of the robot. In this paper, a hybrid optimization strategy for modular robot manipulator dynamic model parameter identification is proposed to accurately identify the dynamic parameters of the robot manipulator. Firstly, the robot dynamics model with Coulomb viscous friction is established. Secondly, the cosine adaptive learning and reversal strategies are introduced to improve the genetic algorithm, and the improved genetic optimization algorithm is applied to optimize the excitation trajectories, and all the robot arm joints are commanded to follow the optimized excitation trajectories. In addition, considering that the Coulomb viscous friction model is not sufficient to accurately express the friction terms, a two-step identification method is proposed by analyzing the sensitivity of the parameters of the Stribeck friction model, combining the significantly identified friction coefficients with the quadratically optimized coefficients of the adaptive inverse genetic algorithm, which solves the problem of lower accuracy caused by the inaccuracy of the friction parameter identification. Then, the dynamic parameters are calculated using the least squares method to determine the system dynamics model information. Finally, the parameter identification and load identification are verified using a 6-degree-of-freedom modular robot manipulator, and the proposed hybrid optimization strategy effectively solves the defect of the low accuracy of the robot manipulator dynamics model compared to the dynamics model moment with Coulomb viscous friction, which in turn improves the control accuracy. Meanwhile, the load identification accuracy can reach 97% depending on the identified dynamics information. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer

关键词： Modular robots

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Compliant control of Lower Limb Rehabilitation Exoskeleton Robot Based on Flexible Transmission

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Journal of Bionic engineering 2023年第3期20卷 1021-1035页

作者： Keping Liu Li Li Wanting Li Jian Gu Zhongbo Sun Department of Control Science and Engineering Changchun University of TechnologyChangchun130012China Department of Electrical and Information Engineering Jilin Engineering Normal UniversityChangchun130052China

To ensure the safety, comfort, and effectiveness of lower limb rehabilitation exoskeleton robots in the rehabilitation training process, compliance is a prerequisite for human–machine interaction safety. First, under the premise of considering the mechanical structure of the lower limb rehabilitation exoskeleton robot (LLRER), when conducting the dynamic transmission of the exoskeleton knee joint, the soft axis is added to ensure that the rotation motion and torque are flexibly transmitted to any position to achieve flexible force transmission. Second, to realize the active compliance control of LLRER, the sliding mode impedance closed-loop controller is developed based on the kinematics and dynamics model of LLRER, and the stability of the designed control system is verified by Lyapunov method. Then the experiment is designed to track the collected bicycle rehabilitation motion data stably, and the algorithm and dynamic model are verified to satisfy the experimental requirements. Finally, aiming at the transmission efficiency and response performance of the soft shaft in the torque transmission process of the knee joint, the soft shaft transmission performance test is carried out to test the soft shaft transmission performance and realize the compliance of the LLRER, so as to ensure that the rehabilitation training can be carried out in a safe and comfortable interactive environment. Through the design of rehabilitation exercise training, it is verified that the LLRER of flexible transmission under sliding mode impedance control has good adaptability in the actual environment, and can achieve accurate and flexible control. During the experiment, the effectiveness of monitoring rehabilitation training is brought through the respiratory belt.

关键词： Lower limb rehabilitation exoskeleton Flexible control Impedance control Stability

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Neural Network for Distributed Collaboration of Multiple Manipulators With Switching Topologies: A Game-Theoretic Perspective

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3213-3221页

作者： Liu, Mei Mu, Wenxin Lv, Xin Sun, Zhongbo Jin, Long Lanzhou University School of Information Science and Engineering Lanzhou730000 China Changchun University of Technology Department of Control Engineering Changchun130000 China

Since distributed control strategies can effectively reduce the operating load of the central processor, they have become a prominent research direction in the field of controlling multiple manipulators. However, existing distributed approaches predominantly rely on fixed topologies, overlooking the dynamic nature of task requirements. To address this limitation, this article proposes a distributed collaboration scheme that incorporates switching topologies. The distributed control problem is further formulated as a game-theoretic framework involving multiple manipulators. A dynamic neural network solver is then developed to approximate the optimal Nash equilibrium strategy, with its convergence and stability proven through theoretical analysis. The effectiveness of the proposed scheme and solver is validated through a series of physical experiments. © 2013 IEEE.

关键词： Redundant manipulators

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Event-triggered optimal interaction control of the MRM system under the complex multi-task constraints: Design and experiments

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Journal of the Franklin Institute 2023年第2期360卷 910-942页

作者： Li, Yuanchun Yao, Ximing Ma, Bing Department of Control Science and Engineering Changchun University of Technology Changchun130012 China

The main challenges of modular robot manipulators (MRMs) with the environmental constraints include the avoidance of catastrophic collision and the precious contacting in the whole interaction process. Consequently, an event-triggered optimal interaction control method of MRMs under the complex multi-task constraints is presented in this paper. Firstly, on the basis of the joint torque feedback (JTF) technique, the dynamic model of constrained MRM subsystem is established. Secondly, the sensorless-based decentralized nonlinear disturbance observer (NDOB) is proposed to detect and identify the sudden external collision for each joint. Then, the performance index function is improved to achieve the interaction control, which contains the fusion state variable function, the influence of external collision, the known model term, and the estimation of model uncertainties through the radial basis function neural network (RBFNN) identifier. Further, based on event-triggered mechanism and adaptive dynamic programming (ADP) algorithm, the approximate event-triggered optimal interaction control strategy is acquired by the critic neural network (NN). Next, the closed-loop MRM system is demonstrated to be uniformly ultimately bounded (UUB) through the Lyapunov stability theorem. Finally, the experiments are achieved effectively for each joint on the platform, such that the feasibility and universality of the proposed interaction control approach are testified by the experimental results. © 2022 The Franklin Institute

关键词： Complex networks

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Privacy-preserving filtering, control and optimization for industrial cyber-physical systems

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science China(Information sciences) 2025年第4期68卷 267-283页

作者： Derui DING Qing-Long HAN Xiaohua GE Xian-Ming ZHANG Jun WANG Department of Control Science and Engineering University of Shanghai for Science and Technology School of Engineering Swinburne University of Technology Department of Computer Science City University of Hong Kong

Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection between cyberspace and physical processes results in the exposure of industrial production information to unprecedented security risks. It is imperative to develop suitable strategies to ensure cyber security while meeting basic performance *** the perspective of control engineering, this review presents the most up-to-date results for privacy-preserving filtering,control, and optimization in industrial cyber-physical systems. Fashionable privacy-preserving strategies and mainstream evaluation metrics are first presented in a systematic manner for performance evaluation and engineering *** discussion discloses the impact of typical filtering algorithms on filtering performance, specifically for privacy-preserving Kalman filtering. Then, the latest development of industrial control is systematically investigated from consensus control of multi-agent systems, platoon control of autonomous vehicles as well as hierarchical control of power systems. The focus thereafter is on the latest privacy-preserving optimization algorithms in the framework of consensus and their applications in distributed economic dispatch issues and energy management of networked power systems. In the end, several topics for potential future research are highlighted.

关键词： industrial cyber-physical systems privacy preservation distributed control distributed optimization power systems

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An artificial systems,computational experiments and parallel execution-based surface electromyogram-driven anti-disturbance zeroing neurodynamic strategy for upper limb human-robot interaction control

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CAAI Transactions on Intelligence technology 2024年第2期9卷 511-525页

作者： Yongbai Liu Keping Liu Gang Wang Jiliang Zhang Yao Chou Zhongbo Sun Department of Mechanical Engineering Changchun University of TechnologyChangchunChina Department of Control Engineering Changchun University of TechnologyChangchunChina School of Electrical and Information Engineering Jilin Engineering Normal UniversityChangchunChina College of Information Science and Engineering Northeastern UniversityShenyangChina Department of Electrical and Computer Engineering Brigham Young UniversityProvoUtahUSA

In recent years,intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation,wherein the human-robot interaction(HRI)control strategy is a momentous part that needs to be ***,the efficacy and robustness of the HRI control algorithm in the presence of unknown external disturbances deserve to be *** deal with these urgent issues,in this study,artificial systems,computational experiments and a parallel execution intelligent control framework are constructed for the HRI *** upper limb-robotic exoskeleton system is re-modelled as an artificial *** on surface electromyogram-based subject's active motion intention in the practical system,a non-convex function activated anti-disturbance zeroing neurodynamic(NC-ADZND)controller is devised in the artificial system for parallel interaction and HRI control with the practical ***,the linear activation function-based zeroing neurodynamic(LAF-ZND)controller and proportionalderivative(posterior deltoid(PD))controller are presented and *** results substantiate the global convergence and robustness of the proposed controller in the presence of different external *** addition,the simulation results verify that the NC-ADZND controller is better than the LAF-ZND and the PD controllers in respect of convergence order and anti-disturbance characteristics.

关键词： neural network pattern recognition robotics signal processing

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Cooperative Game-Based Decentralized Approximate Optimal control for Reconfigurable Robot Manipulators with Physical Human-Robot Interaction 3rd

Cooperative Game-Based Decentralized Approximate Optimal Con...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： An, Tianjiao Yan, Haoyu Ma, Bing Jiang, Hucheng Dong, Bo Department of Control Science and Engineering Changchun University of Technology Changchun China

ISBN: (纸本)9789819616930

A decentralized optimal control strategy that incorporates cooperative game theory is devised for robotic in the context of physical human-robot interaction (pHRI). The primary aim of achieving optimal control in the context of pHRI shifts toward the approximation of Pareto equilibrium by employing a cooperative game framework in which both the human and the reconfigurable robot manipulator (RRM) serve as participants with distinct roles and optimization goals during their interactions. Utilizing the ADP algorithm, a decentralized strategy for approximate optimal control incorporating pHRI has been established. The position error has been confirmed to exhibit UUB. Results from the experiment have been showcased, demonstrating clear advantages. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Microrobots

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Distributed coordination control of multi-agent systems under intermittent sampling and communication: a comprehensive survey

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science China(Information sciences) 2025年第5期68卷 289-331页

作者： Xiaohua GE Qing-Long HAN Xian-Ming ZHANG Derui DING Boda NING School of Engineering Swinburne University of Technology Department of Control Science and Engineering University of Shanghai for Science and Technology Department of Electrical and Electronic Engineering Auckland University of Technology

Sampling and communication are both crucial for coordination in multi-agent systems(MASs), with sampling capturing raw data from the environment for control decision making, and communication ensuring the data is shared effectively for synchronized and informed control decisions across agents. However, practical MASs often operate in environments where continuous and synchronous data samplings and exchanges are impractical, necessitating strategies that can handle intermittent sampling and communication constraints. This paper provides a comprehensive survey of recent advances in distributed coordination control of MASs under intermittent sampling and communication, focusing on both foundational principles and state-of-the-art techniques. After introducing fundamentals, such as communication topologies,agent dynamics, control laws, and typical coordination objectives, the distinctions between sampling and communication are elaborated, exploring deterministic versus random, synchronous versus asynchronous, and instantaneous versus sequential scenarios. A detailed review of emerging trends and techniques is then presented, covering time-triggered, event-triggered,communication-protocol-based, and denial-of-service-resilient coordination control. These techniques are analyzed across various attack models, including those based on data loss, sampled data, time constraints, and topology switching. By synthesizing these developments, this survey aims to equip researchers and practitioners with a clearer understanding of current challenges and methodologies, concluding with insights into promising future directions.

关键词： multi-agent systems distributed coordination control sampled-data control event-triggered control resilient control denial-of-service attacks communication protocols event-triggered mechanisms

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The dynamic relaxation form finding method aided with advanced recurrent neural network

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CAAI Transactions on Intelligence technology 2023年第3期8卷 635-644页

作者： Liming Zhao Zhongbo Sun Keping Liu Jiliang Zhang Department of Mechanical and Electrical Engineering Changchun University of TechnologyChangchunChina Department of Control Engineering Changchun University of TechnologyChangchunChina Department of Electronic and Electrical Engineering The University of SheffieldSheffieldEngland

How to establish a self‐equilibrium configuration is vital for further kinematics and dynamics analyses of tensegrity *** this study,for investigating tensegrity form‐finding problems,a concise and efficient dynamic relaxation‐noise tolerant zeroing neural network(DR‐NTZNN)form‐finding algorithm is established through analysing the physical properties of tensegrity *** addition,the non‐linear constrained opti-misation problem which transformed from the form‐finding problem is solved by a sequential quadratic programming ***,the noise may produce in the form‐finding process that includes the round‐off errors which are brought by the approximate matrix and restart point calculating course,disturbance caused by external force and manufacturing error when constructing a tensegrity ***,for the purpose of suppressing the noise,a noise tolerant zeroing neural network is presented to solve the search direction,which can endow the anti‐noise capability to the form‐finding model and enhance the calculation ***,the dynamic relaxation method is contributed to seek the nodal coordinates rapidly when the search direction is *** numerical results show the form‐finding model has a huge capability for high‐dimensional free form cable‐strut mechanisms with complicated ***,comparing with other existing form‐finding methods,the contrast simulations reveal the excellent anti‐noise performance and calculation capacity of DR‐NTZNN form‐finding algorithm.

关键词： dynamic relaxation form‐finding noise‐tolerant zeroing neural network sequential quadratic programming Tensegrity

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