This research focuses on the integration of a radial basis function neural network (RBFNN) for uncertainty approximation in pneumaticartificial muscle (PAM) systems within the framework of power rate exponential reaching law sliding mode control (PRERL-SMC). Configuredin an antagonistic manner, PAMs provide a range of benefits for developing actuators with human-like characteristics. Nevertheless, theirintrinsic nonlinearity and uncertain behavior are obstacles to attaining accurate control, particularly in rehabilitation scenarios where ensuringcontrol precision is imperative for safety and effectiveness. The proposed method leverages a power rate exponential reaching law to ensurechattering-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 superiorperformance in tracking various trajectories. Notably, our strategy proves robust against external perturbations, demonstrating its applicabilityin rehabilitation scenarios.