Enhance control performance of a pneumatic artificial muscle system using RBF-neural network approximation and power rate exponential reaching law sliding mode control

This research focuses on the integration of a radial basis function neural network (RBFNN) for uncertainty approximation in pneumatic artificial muscle (PAM) systems within the framework of power rate exponential reaching law sliding mode control (PRERL-SMC). Configured in an antagonistic manner, PAMs provide a range of benefits for developing actuators with human-like characteristics.