A reduced-order approach to the adaptive fuzzy sliding mode control of the constrained manipulator

Liang, Yanbing; Shi, Heng; Tian, Guohui

doi:10.1177/1687814018786791

Cited by 2 publications

(4 citation statements)

References 27 publications

(22 reference statements)

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“…The 1-5-1 NN structure is selected with 1 input neuron, 5 hidden neurons, and 1 output neuron for each joint. The NN weights are defined Control parameters α ei 125 β pi 0.5 κ f i 0.9 κ f i (1,2) 0.01 κ f i (3,4) 0.01 κ ai 0.5…”

Section: Establishment Of Experimental Platformmentioning

confidence: 99%

“…The tracking control strategies of MRMs can be classified into centralized control [3,4], distributed control [5,6], and decentralized control [7][8][9] according to the recent literature. The centralized control and distributed control are designed by employing the information of all subsystems the complexity of the dynamics and to improve the generality in practice.…”

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: Establishment Of Experimental Platformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…The dexterous hand can be regarded as the multi-body system. To the multi-body system without constraint, the dynamic equation 24 of n-DOF system can be established as:…”

Section: The Udwadia-kalaba Equationmentioning

confidence: 99%

“…In addition to friction, saturation and other nonlinear factors, there is a strong coupling relationship between the links. The characteristics of sliding mode control [22][23][24] are suitable for the control of dexterous hand. On the one hand, when designing the sliding mode control system, there is no need to do special decoupling for the internal coupling of the system.…”

Section: Introductionmentioning

confidence: 99%

The adaptive neural network sliding mode control for angle/force tracking of the dexterous hand

Qin

Shi²,

Gao³

et al. 2021

Advances in Mechanical Engineering

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In order to achieve high precision control of the dexterous hand, an adaptive neural network sliding mode control algorithm based on the U-K (Udwadia-Kalaba) equation is proposed. Firstly, based on the U-K equation and considering the ideal and non-ideal constrained force at each link of the dexterous hand, the detailed dynamic equation is derived. Secondly, considering the uncertainty of the non-ideal constrained force (mainly the friction force on each link of the dexterous hand) and the chattering phenomenon when using sliding mode control alone, the adaptive neural network and the sliding mode control algorithm are combined to realize the high-precision tracking and estimation of each link angle trajectory and the non-ideal constrained force. Finally, in order to verify the correctness and rationality of the proposed algorithm, the 3-DOF spatial dexterous hand is taken as the simulated object. The simulation results show that the tracking and estimation errors of each link angle and the non-ideal constrained force are 10−2 order of magnitude.

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A reduced-order approach to the adaptive fuzzy sliding mode control of the constrained manipulator

Cited by 2 publications

References 27 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

The adaptive neural network sliding mode control for angle/force tracking of the dexterous hand

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