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Nonzero-sum game-based decentralized approximate optimal control of modular robot manipulators with coordinate operation tasks using value iteration

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第2期36卷 026209-026209页

作者： Dong, Bo Gao, Yuhang An, Tianjiao Jiang, Hucheng Ma, Bing Changchun Univ Technol Dept Control Sci & Engn Changchun Peoples R China

Accurate trajectory tracking and appropriate contact force are crucial for the coordinated operation-oriented control of modular robot manipulators (MRMs). Considering the practical need for precision in system control, resource optimization, and disturbance compensation within the context of the coordinated operation tasks (COTs) of MRMs, this paper employs a value iteration (VI) technique to devise a decentralized approximate optimal control strategy grounded in nonzero-sum game (NZSG) theory. To obtain more accurate, reliable, and safe control, a dynamic model of the MRM is established using joint torque feedback technology;then, the problem of optimal control for MRM systems focused on coordinated operation-oriented control is reformulated as an NZSG involving multiple subsystems. The present study, grounded in the theoretical framework of the adaptive dynamic programming (ADP) algorithm, employs an event-triggered NZSG strategy, utilizing VI to resolve the coupled Hamilton-Jacobian equations, culminating in the derivation of the Nash equilibrium solutions. Through stringent stability analysis, it is established that the trajectory tracking error for the closed-loop MRM system engaged in COTs is uniformly ultimately bounded. The proposed method's efficacy is subsequently corroborated through experimental validation.

关键词： adaptive dynamic programming optimal control modular robot manipulators nonzero-sum game value iteration

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A Novel Decentralized Fixed-time Tracking Control for modular robot manipulators: Theoretical and Experimental Verification

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2023年第9期21卷 3036-3047页

作者： Lu, Zengpeng Li, Yuanchun Li, Yan Changchun Univ Technol Sch Elect & Comp Engn Changchun 130012 Peoples R China

This paper presents a novel decentralized fixed-time tracking control approach, which realizes the advantages of modular robot manipulators (MRMs) with fixed-time convergence, strong robustness, and high tracking performance. First, to estimate the total uncertainty of MRMs, the fixed-time observer based on the extended state is developed. Then, combined with the disturbance observer, a novel decentralized control method based on a fixed-time control strategy was devised to accomplish global fixed-time convergence of MRMs. And, stability analysis based on Lyapunov is utilized to obtain the fixed-time stability as well as convergence time of MRMs. Finally, numerical analysis and experiment respectively verify the excellent tracking ability of the presented decentralized fixed-time tracking control.

关键词： Disturbance observer fixed-time convergence Lyapunov stability modular robot manipulators

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Value Iteration-Based Adaptive Fuzzy Backstepping Optimal Control of modular robot manipulators via Integral Reinforcement Learning

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2024年第4期26卷 1347-1363页

作者： Dong, Bo Jiang, Hucheng Cui, Yiming Zhu, Xinye An, Tianjiao Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Peoples R China

An adaptive fuzzy backstepping optimal control method is developed for modular robot manipulators (MRMs) via value iteration (VI). This paper adopts joint torque feedback (JTF) technique to construct subsystem dynamics model, and the state space description is deduced. According to fusion function which contains the error in joint angular velocity and position, the cost function is established. The integral reinforcement learning (IRL) is integrated into the VI algorithm, which solves the optimal tracking control issue without system drift dynamics. For purpose of improving the control effect, the optimal tracking control issue of manipulator can be reconsidered as the optimal compensation issue which adopting the local dynamics information. Then the uncertainty in the model can be compensated by an adaptive fuzzy backstepping compensation controller which is constructed by fuzzy logic system (FLS) and backstepping control method. The optimal compensation control strategy is adopted to deal with the interconnected dynamic coupling (IDC), which contains global information about each joint. Based on the VI algorithm and adaptive dynamic programming (ADP) method, an effective solution of Hamiltonian-Jacobi-Bellman (HJB) equation is presented. According to Lyapunov theorem, the trajectory tracking error is uniformly ultimately bounded (UUB) by using the adaptive fuzzy backstepping optimal control method. Finally, the effectiveness of the proposed method is verified by experiments.

关键词： Integral reinforcement learning Adaptive dynamic programming Value iteration modular robot manipulators Fuzzy logic system

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Decentralized robust zero-sum neuro-optimal control for modular robot manipulators in contact with uncertain environments: theory and experimental verification

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NONLINEAR DYNAMICS 2019年第1期97卷 503-524页

作者： Dong, Bo An, Tianjiao Zhou, Fan Liu, Keping Li, Yuanchun Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Jilin Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper presents a decentralized robust zero-sum optimal control approach for modular robot manipulators (MRMs) in contact with uncertain environments based on the adaptive dynamic programming (ADP) algorithm. The dynamic model of MRMs is formulated via joint torque feedback technique that is deployed for each joint module to design the model compensation controller. An uncertainty decomposition-based robust control is developed to compensate the model uncertainties, and then, the robust optimal control problem of the MRM system is transformed into a two-player zero-sum optimal control one. According to the ADP algorithm, the Hamilton-Jacobi-Isaacs equation can be solved by establishing action and critic neural networks, thus making the derivation of the approximate optimal control policy feasible. Based on the Lyapunov theory, the closed-loop robotic system is proved to be asymptotic stable under the developed decentralized control method. Finally, experiments are conducted to verify the effectiveness and advantages of the proposed method.

关键词： modular robot manipulators Adaptive dynamic programming Decentralized control Optimal control Zero-sum game

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Data-driven Multiplayer Mixed-zero-sum Game Control of modular robot manipulators with Uncertain Disturbance

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2023年第2期21卷 645-657页

作者： Zhu, Xinye An, Tianjiao Dong, Bo Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Peoples R China Baicheng Normal Univ Baicheng 137000 Peoples R China

This paper develops a data-driven multiplayer mixed-zero-sum game control approach of modular robot manipulators (MRMs) with uncertain disturbance via adaptive dynamic programming (ADP). The dynamic model of MRMs is formulated via joint torque feedback. We deem n modules and uncertain disturbance as players in the game theory structure. The uncertainties in the MRM system such as joint friction and interconnected dynamic couplings (IDCs), we employ data-driven model based on recurrent neural networks (RNNs) to built. According to ADP, the Hamilton-Jacobi (HJ) equation can be solved by using critic neural networks and derivate the optimal control law. The closed-loop robotic system is proved to be asymptotic stable via mixed-zero-sum game control. Experiments are conducted to verify the effectiveness.

关键词： Adaptive dynamic programming mixed-zero-sum modular robot manipulators neural networks

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Actor-Critic-Identifier Structure-Based Decentralized Neuro-Optimal Control of modular robot manipulators With Environmental Collisions

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IEEE ACCESS 2019年 7卷 96148-96165页

作者： Dong, Bo An, Tianjiao Zhou, Fan Liu, Keping Yu, Weibo Li, Yuanchun Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Jilin Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper presents a decentralized zero-sum optimal control method for MRMs with environmental collisions via an actor-critic-identifier (ACI) structure-based adaptive dynamic programming (ADP) algorithm. The dynamic model of the MRMs is formulated via a novel collision identification method that is deployed for each joint module, in which the local position and torque information are used to design the model compensation controller. A neural network (NN) identifier is developed to compensate the model uncertainties and then, the optimal control problem of the MRMs with environmental collisions can be transformed into a two-player zero-sum optimal control one. Based on the ADP algorithm, the HamiltonJacobi-Isaacs (HJI) equation is solved by constructing the actor-critic NNs, thus making the derivation of the approximate optimal control policy feasible. Based on the Lyapunov theory, the closed-loop robotic system is proved to be asymptotically stable. Finally, the experiments are conducted to verify the effectiveness and advantages of the proposed method.

关键词： Adaptive dynamic programming collision identification decentralized optimal control modular robot manipulators zero-sum game

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Tansig type NLESO-based integral terminal sliding mode control for modular robot manipulators with uncertain disturbance: Theory and experimental verification

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2024年第2期46卷 395-406页

作者： Dong, Bo Cao, Yuxin Cui, Yiming Wang, Tianhe An, Tianjiao Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Peoples R China

To overcome the mutual constraints between the system chattering and the arrival time in conventional sliding mode control in this paper, a tangent excitation function (tansig) type nonlinear extended state observer (NLESO)-based integral terminal sliding mode control is proposed to solve the trajectory tracking control task of the modular robot manipulators (MRMs) possessing strong coupling and complex time-varying properties. Through the joint torque feedback technology, the MRM system dynamic model is formulated;on this basis, the proposed NLESO based on tansig is utilized to estimate and compensate the system uncertain information, which are consisted of the friction, interconnected dynamic coupling, and external disturbance. The proposed NLESO can simplify the traditional extended state observer design parameters for theoretical analysis and practical application. The proposed tansig-type NLESO-based integral terminal sliding mode control integrated adaptive term exponential reaching law for MRMs is designed to diminish the arrival time when the system state converges to the equilibrium point. The presented algorithm improves the robotic system antidisturbance ability. Furthermore, the Lyapunov theory is utilized to verify stability of the proposed NLESO and closed-loop robotic system. Finally, the availability of the proposed algorithm is demonstrated by experiments.

关键词： modular robot manipulators model uncertainty nonlinear extended state observer integral terminal sliding mode control exponential reaching law

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Event-triggered adaptive fuzzy optimal control of modular robot manipulators using zero-sum differential game through value iteration

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2023年第9期37卷 2364-2379页

作者： Dong, Bo Feng, Zhian Cui, Yiming Zhu, Xinye An, Tianjiao Changchun Univ Tehnol Dept Control Sci & Engn Changchun Peoples R China Changchun Univ Tehnol Dept Control Sci & Engn Yanan Ave 2055 N Changchun Peoples R China

In this article, a novel event-triggered zero-sum differential game through value iteration (VI)-based adaptive fuzzy control is proposed for modular robot manipulators (MRMs) with uncertain disturbance. The subsystem dynamic of MRMs is established by joint torque feedback (JTF) technology. An identifier based on fuzzy logic model (FLM) is established to identify unknown system dynamics. Then, the trajectory tracking control issue of MRMs with uncertain disturbance is transformed into a two-player zero-sum differential game. On the basis of event-triggered mechanism and VI algorithm, the event-triggered Hamilton-Jacobi-Issacs (HJI) equation is approximately solved through establishment of actor-critic structure. Different from the traditional adaptive dynamic programming (ADP) algorithm, this scheme is based on adaptive fuzzy optimal control through an actor-critic structure, which refers to adaptation of both optimal cost function and optimal control policy. Moreover, tracking errors of closed-loop MRM system are demonstrated to be uniformly ultimately bounded (UUB) under Lyapunov stability theorem. At last, experimental examples and comparisons show the reliability and effectiveness of this method.

关键词： adaptive dynamic programming event-triggered mechanism fuzzy optimal control modular robot manipulators value iteration zero-sum differential game

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Multiplayer zero-sum games optimal control for modular robot manipulators with interconnected dynamic couplings

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2022年第12期36卷 3254-3270页

作者： Zhu, Xinye An, Tianjiao Dong, Bo Changchun Univ Technol Dept Control Sci & Engn Changchun Peoples R China

This article presents a multiplayer zero-sum game optimal control for modular robot manipulators (MRMs) via adaptive dynamic programming (ADP). The dynamic model of MRMs with interconnected dynamic couplings (IDCs) is formulated. The control law and IDC effect are regarded as players in each module. The optimal tracking control problem of the MRM system can be transformed into a multiplayer zero-sum game. Base on ADP algorithm, the Hamilton-Jacobi-Issacs (HJI) equation is solved via critic neural network. Then, the optimal control law can be derivated. The closed-loop robotic system is proved to be asymptotic stable and experiments are conducted to verify the effectiveness of the control method.

关键词： adaptive dynamic programming modular robot manipulators multiplayer zero-sum game optimal control

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Decentralized Robust Active Disturbance Rejection Control of modular robot manipulators: An Experimental Investigation With Emotional pHRI

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IEEE ACCESS 2022年 10卷 38625-38634页

作者： Pang, Xiao Men, Xiaodong Dong, Bo An, Tianjiao Changchun Guanghua Univ Inst Sci Res & Acad Changchun 130033 Peoples R China Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Peoples R China Fulsci Automot Elect Co Ltd Changchun 130014 Peoples R China

This paper presents an active disturbance rejection control (ADRC) method for modular robot manipulators (MRMs) based on extended state observer (ESO), which solves the problem of trajectory tracking when modular robot manipulators facing the emotional physical human-robot interaction (pHRI). The dynamic model of MRMs is formulated via joint torque feedback (JTF) technique that is deployed for each joint module to design the model compensation controller. ESO is used to estimate the interconnected dynamic coupling (IDC) term and the interference term caused by emotional pHRI. An uncertainty decomposition-based robust control is developed to compensate the friction term. The terminal sliding mode control (TSMC) algorithm is introduced to the controller to provide faster convergence and higher precision control effect. Based on the Lyapunov theory, the tracking error is proved to be ultimately uniformly bounded (UUB). Finally, experiments demonstrate advantages of the proposed method.

关键词： robots Friction manipulators Torque Robust control Uncertainty Trajectory tracking modular robot manipulators extended state observer terminal sliding mode emotional pHRI active disturbance rejection control

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