Adaptive Integral-Type Neural Sliding Mode Control for Pneumatic Muscle Actuator

Abstract

This paper presents an integral-type adaptive sliding mode controller integrated into a neural network for position-tracking control of a pneumatic muscle actuator testing system. Stability of the closed-loop system is covered by the sliding mode algorithm while both control error and control energy are minimized by the neural network. With only four weight factors in the hidden layer and two weight factors in the output layer, the network provides a very high calculation speed. Then, the approach is successfully verified on a real-time system under different working conditions. By comparing it with a proportional-integral-differential controller on the same system and under the same working conditions, the effectiveness of the designed controller is confirmed.