Robust nonlinear tracking control design for IPMC using Neural Network based sliding mode approach

In this paper, a robust nonlinear tracking control design for an ionic polymer metal composite (IPMC) with uncertainties is proposed by using Neural Network based sliding mode approach. The IPMC, also called artificial muscle, is a novel smart polymer material, and many potential applications for low mass high displacement actuators in biomedical and robotic systems have been shown. In general, the IPMC has highly nonlinear property, and there exist uncertainties caused by identifying some physical parameters and approximate calculation in dynamic model. Moreover, the control input is subject to some constraints to ensure safety and longer service life of IPMC. As a result, for a nonlinear dynamic model with uncertainties and input constraints, an IPMC artificial muscles position tracking control system based on sliding mode control approach is presented, where, the exponential reaching law is used to design sliding mode controller, a saturation function is used in the sliding mode control law design to suppress chattering. The robust stability can be guaranteed. Moreover, in order to improve tracking performance, a quickly and precisely robust tracking system to the stabilized system is designed and the parameters of tracking controller are optimized by using Neural Network. Finally, the effectiveness of the proposed method is confirmed by simulation results.