RBF Network Adaptive Control Based on SMC Compensation for Six-axis Manipulator

doi:10.12068/j.issn.1005-3026.2017.11.017

Abstract

Abstract: A RBF neural network adaptive control based on sliding mode control compensation is proposed， and applied to the six-axis robot manipulator， to achieve high-precision position tracking and rapid approaching speed in the case of model uncertainty. Sliding mode control is adopted as auxiliary for system robust compensation and fuzzy control is used for real-time switching gain changing to make the uncertainties better， in order to keep the system′s stability toward the influence of friction， external interference errors and parameter changes. Adaptive laws weights were adjusted constantly online， and Lyapunov theorem was used to prove the stability. The simulation result indicated that a faster convergence rate and stronger robustness can be acquired with the proposed control algorithm， comparing with other research. It also shows that in reality， significant difference exists between the modeling parameters and the actual values.

Key words: RBF network, SMC, fuzzy control, Lyapunov stability, six-axis manipulator

CLC Number:

TP242

WANG Hong， ZHENG Tian-qi. RBF Network Adaptive Control Based on SMC Compensation for Six-axis Manipulator[J]. Journal of Northeastern University Natural Science, 2017, 38(11): 1601-1606.

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