Sliding mode control of antagonistically coupled pneumatic artificial muscles using radial basis neural network function

Abstract This study presents a novel approach to enhance the control of Pneumatic Artificial Muscle (PAM) systems by combining Sliding Mode Control (SMC) with the Radial Basis Function Neural Network (RBFNN) algorithm. PAMs, when configured antagonistically, offer several advantages in creating human-like actuators. However, their inherent nonlinearity and uncertainty pose challenges for achieving precise control, especially in rehabilitation applications where control quality is crucial for safety and efficacy. To address these challenges, we propose an RBF-SMC approach that leverages the nonlinear elimination capability of SMC and the adaptive learning ability of RBFNN. The integration of these two techniques aims to develop a robust controller capable of effectively dealing with the inherent disadvantages of PAM systems under various operating conditions. The suggested RBF-SMC approach is theoretically verified using the Lyapunov stability theory, providing a solid foundation for its effectiveness. To validate its performance, extensive multi-scenario experiments were conducted, serving as a significant contribution of this research. The results demonstrate the superior performance of the proposed controller compared to conventional controllers in terms of convergence time, robustness, and stability. This research offers a significant contribution to the field of PAM system control, particularly in the context of rehabilitation. The developed RBF-SMC approach provides an efficient and reliable solution to overcome the challenges posed by PAMs' nonlinearity and uncertainty, enhancing control quality and ensuring the safety and efficacy of these systems in practical applications.