|Robust Adaptive Sliding Mode Neural Networks Control for Industrial Robot Manipulators
Vu Thi Yen*, Wang Yao Nan, and Pham Van Cuong
International Journal of Control, Automation, and Systems, vol. 17, no. 3, pp.783-792, 2019
Abstract : "This paper proposes an original robust adaptive controller by using Radial Basis Function Neural networks
(RBFNNs) for industrial robot manipulators (IRMs) in uncertain dynamical environments. This suggested
control structure combines sliding mode technique, RBFNNs approximation and adaptive technique to improve the
high accuracy of the tracking control. The proposed RBFNNs can deal the small problems successful because of
its simple structure, faster training update laws and better approximation for the unknown dynamic of IRMs. All
the parameters of the proposed control system are determined by Lyapunov stability theorem, and tuned online by
an adaptive learning algorithm. Therefore, the stability, robustness and desired tracking performance of RBFNNs
for IRMs are guaranteed. The simulations and experimental performed on a three-link IRMs are proposed in comparison
with proportional integral differential (PID) and adaptive Fuzzy (AF) control to prove the robustness and
efficiency of the RBFNNs."
Adaptive control, industrial robot, neural networks, RBF network.