The Parallel Pneumatic Artificial Muscle Platform Based on RBF Neural Network Compensation

Abstract

A two-degree-of-freedom parallel mechanism control system based on an adaptive learning rate and radial basis function (RBF) neural network controller is studied in this paper. The mechanism is composed of four pneumatic artificial muscles(PAM), forming two pairs of antagonistic single-degree-of-freedom joints, which enable two-degree-of-freedom motion along the X and Y axes. The core objective of the system is to automatically output the air pressure values for the X and Y axes based on the input desired angle, driving the joints to precisely reach the specified angle. In this research, dynamic modeling of the two pairs of driving joints composed of four pneumatic muscles was conducted, analyzing the motion characteristics of the system. Subsequently, an RBF neural network was employed to approximate system modeling errors and external disturbances, combined with a PID controller to optimize the driving performance of the pneumatic muscles. The stability of the controller was proven by designing the Lyapunov function, ensuring that the system remains stable during dynamic changes. Finally, simulation experiments were conducted using MATLAB/Simulink to verify the effectiveness of the proposed algorithm. The experimental results demonstrate that the control algorithm enables the actual angle to track the desired angle in real-time, with high control accuracy and stability. This research provides a new solution for the precise control of pneumatic muscle-driven parallel joint systems, with broad application prospects, effectively addressing the limitations of traditional PAM control methods that require precise modeling and suffer from poor robustness.