This study investigates the impact of inner-loop pressure regulation on the dynamic performance of pneumatic artificial muscle (PAM) systems using a dual-loop control architecture. Three pressure control strategies – Proportional-Integral (PI), Proportional-Integral-Derivative (PID), and Radial Basis Function neural network-tuned PID (RBF-PID) – are experimentally evaluated in terms of tracking accuracy, transient response, and disturbance rejection. Results show that the RBF-PID controller achieves the highest accuracy of pressure tracking , with a root-mean-square error (RMSE) of 0.067 bar under a modulated sinusoidal input, outperforming PID (0.088 bar) and PI (0.094 bar) controllers. In position control tasks, all dual-loop configurations offer improved stability compared to the single-loop setup. The RBF-PID controller further enhances performance, achieving a settling time of 3.04 seconds, zero overshoot, and the shortest recovery time of 2.73 seconds under a 10-kg load disturbance. Although the performance gap between PI and PID remains modest, suggesting PI remains a practical solution for resource-constrained applications, the RBF-PID controller provides significant benefits in adaptability and robustness. These findings underscore the importance of adaptive pressure regulation in improving the tracking accuracy and resilience of PAM-based actuators. The choice of control strategy should therefore be guided by the specific application context, balancing control performance with computational and hardware constraints.