Intelligent Control System based on Wavelet Type-2 Fuzzy Neural network Design For Robot System

Abstract

In this paper, we propose a wavelet type-2 fuzzy brain imitated controller (WT2FBIC) for nonlinear robotic systems. The suggested method combines a wavelet type-2 fuzzy system (WT2FS) and a brain imitated controller (BIC) to improve learning efficiency. The system's inputs, which comprise a sensory and an emotional channel, eventually lead to the network's output. The WT2FBIC parameter update rules use the Lyapunov theory and the gradient descent method. To correct for the WT2FBIC in a main controller, a robust controller can be used for compensation. Robots find applications in a wide variety of industries thus the proposed WT2FBIC-based control system is used to control nonlinear robotic systems. In this work, a two-jointed robotic manipulator control system used the proposed method is demonstrated. The comparison of the proposed method with recent methods point out the effectiveness of the proposed method. The simulation results indicate that the proposed control approach provides good control performance.