Enhance Control Performance of a Pneumatic Artificial Muscle System Using RBF-Neural Network Approximation and Power Rate Exponential Reaching Law Sliding Mode Control

Tóm tắt

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. Nevertheless, their
intrinsic nonlinearity and uncertain behavior are obstacles to attaining accurate control, particularly in rehabilitation scenarios where ensuring
control precision is imperative for safety and effectiveness. The proposed method leverages a power rate exponential reaching law to ensure
chattering-free control and swift convergence towards desired trajectories, while the RBFNN effectively approximates system uncertainties.
Through comprehensive experiments, we compare the RBF-PRERL-SMC approach with conventional control methods, showcasing its superior
performance in tracking various trajectories. Notably, our strategy proves robust against external perturbations, demonstrating its applicability
in rehabilitation scenarios.