ADAPTIVE ROBUST CONTROLLER BASED ON NEURAL NETWORKS FOR INDUSTRIAL ROBOT MANIPULATOR
Abstract
This paper proposed an adaptive robust controller based on neural networks
for industrial robot manipulator (IRM). In fact, robot manipulators are a
nonlinear system and in the working process, they usually bear the nonlinear
fiction, payload variation external disturbance, etc. To deal with these problems,
an intelligent controller which is designed based on inheriting the advantages of
the robust adaptive NNs and SMC scheme to investigate to the joint position
control of industrial robot manipulator. Here, the ARNNs are used to approximate
the unknown dynamics without the requirement of prior knowledge. In addition,
sliding mode control (SMC) is a well-known nonlinear control strategy because
of its robustness. A robust term function is selected as an auxiliary controller to
guarantee the stability and robustness under various environments. The
adaptation laws for the weights of the ARNNs are adjusted using the Lyapunov
stability theorem such that the stability of the proposed control systems is
guaranteed. The effectiveness and robustness of the proposed methods are
demonstrated by comparative simulation results.