A PSO optimized RBFNN and STSMC scheme for path tracking of robot manipulator

This article presents the design of super twisting sliding mode control (STSMC) based on radial basis function neural network (RBFNN) for path tracking two link robot manipulator. The proposed controller is utilized to guarantee and achieve that surface can be in equilibrium point within a short time avoid problem chattering at output. Lyapunov theory used presenting new convergence proof. Also, particle swarm optimization (PSO) algorithm employed give optimal parameter values controller. Simulation results explain goodness method trajectory 2-link manipulator when compared with SMC strategy. Results demonstrate percentage improvement mean square error (MSE) using STSMC standard are 15.36%, 16.94% 12.92%, three different cases respectively.