Decentralized robust optimal control for modular robot manipulators via critic-identifier structure-based adaptive dynamic programming

Dong, Bo; Zhou, Fan; Liu, Keping; Li, Yuanchun

doi:10.1007/s00521-018-3714-8

Cited by 20 publications

(14 citation statements)

References 54 publications

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“…The joint friction torque f ri (q i ,q i ) mainly reflects the friction of the motor and speed reducer. Motivated by [40,41], it is assumed to be a function of the joint position and joint velocity as:…”

Section: Dynamic Model and Preliminariesmentioning

confidence: 99%

“…Supposing the nominal values of f bi , f si , f τ i and f ci are closed to their actual values, then according tothe lineariza-tion scheme [41], the friction model (2) can be approximated by:…”

Section: Dynamic Model and Preliminariesmentioning

confidence: 99%

“…Remark 3 On the basis of optimization theory and the dynamic model analysis, in this paper, the decentralized tracking control problem of MRMs is transformed into an optimal compensation control problem (13), which consists of model-based robust control u 1i (ϑ i ) and ADP-based optimal control u * 2i (ϑ i ). Inspired by the previous works [41,44], the decentralized optimal tracking control is developed with a compensator-critic structure, which can not only mitigate model uncertainties in real time but also realize the satisfactory tracking performance for MRMs.…”

Section: Dynamic Model and Preliminariesmentioning

confidence: 99%

“…He et al [34] designed a decentralized event-triggered control method for nonlinear systems with matched interconnections. For the MRM systems, Dong et al [35] proposed the time-triggered decentralized robust optimal control for MRMs via critic-identifier structure-based ADP approach. Zhao et al [36] developed an event-triggered decentralized tracking optimal control approach by employing a local NN observer to estimate unknown model dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Compensator-critic structure-based event-triggered decentralized tracking control of modular robot manipulators: theory and experimental verification

2021

Complex Intell. Syst.

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This paper presents a novel compensator-critic structure-based event-triggered decentralized tracking control of modular robot manipulators (MRMs). On the basis of subsystem dynamics under joint torque feedback (JTF) technique, the proposed tracking error fusion function, which includes position error and velocity error, is utilized to construct performance index function. By analyzing the dynamic uncertainties, a local dynamic information-based robust controller is designed to engage the model uncertainty compensation. Based on adaptive dynamic programming (ADP) algorithm and the event-triggered mechanism, the decentralized tracking control is obtained by solving the event-triggered Hamilton–Jacobi–Bellman equation (HJBE) with the critic neural network (NN). The tracking error of the closed-loop manipulators system is proved to be ultimately uniformly bounded (UUB) using the Lyapunov stability theorem. Finally, experimental results illustrate the effectiveness of the developed control method.

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Section: Dynamic Model and Preliminariesmentioning

confidence: 99%

Section: Dynamic Model and Preliminariesmentioning

confidence: 99%

Section: Dynamic Model and Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Compensator-critic structure-based event-triggered decentralized tracking control of modular robot manipulators: theory and experimental verification

2021

Complex Intell. Syst.

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“…Zhao et al 28 proposed a proportional-integral (PI) algorithm-based decentralized scheme for large systems with mismatched interconnections. Dong et al 29 addressed the optimal control problems of MRRs by combining the modelbased compensation control with ADP-based learning control, and their researches are further expanded to deal with the optimal tracking control issues of MRRs with uncertain environments. 30 The ADP-based decentralized methods are proposed to solve the stabilization control problem of complex robot manipulators and nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Model-free optimal decentralized sliding mode control for modular and reconfigurable robots based on adaptive dynamic programming

Dong

Zhou

et al. 2019

Advances in Mechanical Engineering

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In this article, a model-free decentralized sliding mode control method is proposed based on adaptive dynamic programming algorithm to solve the problem of optimal trajectory tracking control of modular and reconfigurable robots. The dynamic model of modular and reconfigurable robot is formulated by a synthesis of joint subsystems with interconnected dynamic couplings. Based on sliding mode control technique, the optimal control problem of the modular and reconfigurable robot systems is transformed into an optimal compensation issue of unknown dynamics of each joint subsystems, in which the interconnected dynamic couplings effects among the subsystems are approximated by using the developed neural network identifier. Based on policy iteration scheme and the adaptive dynamic programming algorithm, the Hamilton-Jacobi-Bellman equation can be solved by using the critic neural network, so that optimal control policy can be obtained. The closed-loop system is proved to be asymptotically stable by using the Lyapunov theory. Finally, simulation results are provided to demonstrate the effectiveness of the method.

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

Zhu

Dong

2022

Adaptive Control & Signal

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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.

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Decentralized robust optimal control for modular robot manipulators via critic-identifier structure-based adaptive dynamic programming

Cited by 20 publications

References 54 publications

Compensator-critic structure-based event-triggered decentralized tracking control of modular robot manipulators: theory and experimental verification

Compensator-critic structure-based event-triggered decentralized tracking control of modular robot manipulators: theory and experimental verification

Model-free optimal decentralized sliding mode control for modular and reconfigurable robots based on adaptive dynamic programming

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

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