Adaptive PID tracking control based radial basic function networks for a 2-DOF parallel manipulator

Abstract

In this paper, an adaptive proportional integral derivative (PID) based on radial basic function neural networks (APID-RBFNs) is proposed for tracking control of a 2 degree of freedom (DOF) parallel manipulator. For developing the controller, a dynamic model of this parallel manipulator is developed based on a matrix form equations. APID-RBFNs is designed to overcome external disturbances and complex noises acting on the parallel manipulator system by using adaptive PID sliding surface with RBFNs. By using the Lyapunov method, the stability of the overall system with full state constraints is proved. The simulation results in universal software Matlab/Simulink show that the proposed control strategy has better dynamic performance and robustness than conventional PID tracking control.