Adaptive Dynamic Surface Control of Flexible-Joint Robots Using Self-Recurrent Wavelet Neural Networks

A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRW...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2006-12, Vol.36 (6), p.1342-1355
Main Authors: Yoo, Sung Jin, Park, Jin Bae, Choi, Yoon Ho
Format: Article
Language:English
Subjects:
Actuators
Adaptive control
Adaptive control systems
Computer simulation
Control systems
Dynamic surface control (DSC)
Dynamical systems
Dynamics
Explosions
flexible-joint robots
Neural networks
Programmable control
Robot control
Robots
Robust control
self-recurrent wavelet neural network (SRWNN)
Surface waves
Uncertainty
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Summary:A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides the ability to overcome the "explosion of complexity" problem in backstepping controllers. The SRWNNs are used to observe the arbitrary model uncertainties of FJ robots, and all their weights are trained online. From the Lyapunov stability analysis, their adaptation laws are induced, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a three-link FJ robot are utilized to validate the good position tracking performance and robustness against payload uncertainties and external disturbances of the proposed control system
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2006.875869