NMPC design for a self-aligning compliant gait rehabilitation robot

Abstract

The application of robotic devices in rehabilitation is proliferating. Such devices’ mechanism design, actuation, and control strategy are essential for effective and successful rehabilitation treatment. This paper investigates the effectiveness of a self-aligning mechanism for a multi-DOFs (Degrees of Freedom) rehabilitation robot. The actuation is provided by lightweight albeit powerful Pneumatic Muscle Actuators (PMA). Although the mechanism design and the actuation system provide a safe, secure, and efficient platform for rehabilitation, they increase the complexity of the system modeling and, subsequently, the control system’s design. Furthermore, the mechanism has three active and five passive DOFs, which further increase the intricacies of system identification. Hence, this paper presents an autodidactic approach to identify the system dynamics using the Koopman operator. The learned operator is then integrated with the Nonlinear Model Predictive Controller (NMPC) to guide the robot along the predefined path while adapting to the nonlinear dynamics of the physical human-robot interaction. Finally, the rehabilitation robot and the control scheme were experimentally validated with healthy human subjects. The results demonstrate that the NMPC controller could successfully manipulate the gait rehabilitation robot with the subject to achieve the desired orientation during the entire gait cycle.