A novel adaptive feed-forward-PID controller of a SCARA parallel robot using pneumatic artificial muscle actuator based on neural network and modified differential evolution algorithm

Abstract

This paper proposes a novel control system combining adaptively feed-forward neural controller and PID controller to control the joint-angle position of the SCARA parallel robot using the pneumatic artificial muscle (PAM) actuator. Firstly, the proposed inverse neural NARX (INN) model dynamically identifies all nonlinear features of the SCARA parallel PAM robot. Parameters of the inverse neural NARX model are optimized with the modified differential evolution (MDE) algorithm. Secondly, combining the inverse neural NARX model that provides a feed-forward control value from the desired joint position and the conventional PID controller applied to improve the precision and reject the steady state error in the joint position control. Finally, the new adaptive back-propagation (aBP) algorithm, based on Sugeno fuzzy system, proposed for online updating the weight values of the inverse neural NARX model as to adapt well to the disturbances and dynamic variations in its operation. Experimental tests confirmed the performance and merits of the proposed control scheme in comparison with the traditional control methods.