Self-Adaptive PID Control Based on RBF Network for Trajectory Tracking of Dual-Mass Servo System

This paper presents a state-of-the-art algorithm of self-adaptive RBF network PID control and aims at achieving the precise and fast trajectory tracking for a dual-mass servo system. To increase the robustness of servo system parameter varying and external disturbances, a classical PID algorithm with enhanced structure based on RBF neural network is proposed. Extensive simulations show that it practically validates the superiority of the proposed RBF adaptive PID controller. The experiment was simulated respectively under the external disturbance, which indicates that accurate tracking performance of the servo system with dual-mass load has been achieved and also verifies the effectiveness of self-adaptive PID control strategy.