Trajectory Tracking Control of an Industrial Robot Manipulator Using Fuzzy SMC with RBFNN

Abstract

One of the main problems associated with Sliding Mode Control (SMC) is that a whole knowledge of the system dynamics and system parameters is required to compute the equivalent control. Neural networks are popular tools for computing the equivalent control. In fuzzy SMC with Radial Basis Function Neural Network (RBFNN), a Lyapunov function is selected for the design of the SMC and RBFNN is proposed to compute the equivalent control. The weights of the RBFNN are adjusted according to an adaptive algorithm. Fuzzy logic is used to adjust the gain of the corrective control of the SMC. Proposed control method and a PID controller are tested on the Manutec-r15industrial robot manipulator. The real time implementations indicate that the proposed method can be applied to trajectory control applications of robot manipulators.