Design of a Tube Model Predictive Controller for Pneumatic Muscle Actuators Servomechanism under Uncertainty

Pneumatic muscle actuators (PMAs) are efficient and versatile, mimicking natural muscle-tendon systems. They are widely used in manufacturing, automotive, and aerospace industries due to their lightweight design, high force-to-weight ratio, and flexibility, making them suitable for delicate tasks and powerful exertions. Their application in industrial and biomechatronic systems highlights their potential to revolutionize automation and human-machine interaction. This research aimed to develop a PMA regulator using the Model Predictive Control (MPC) framework. Researchers identified key PMA characteristics and defined attributes for the regulator, ensuring accurate signal tracking, effective uncertainty handling, and real-time operation. The initial stages involved implementing a proportional-integral-derived (PID) controller, and by integrating a PID block with a linear MPC block, a tube MPC controller was developed to enhance performance. The controllers’ responses were tested in noisy environments using a low-pass filter for evaluation. The tube MPC controller demonstrated optimal control, robustness, and expertise in managing uncertainties, making it a reliable choice for system control.