Design and Implementation of a Real-Time Nonlinear Model Predictive Controller for a Lower Limb Exoskeleton with Input Saturation

In this paper, a nonlinear model predictive controller (NMPC) with input saturation is designed and modified for a rehabilitative exoskeleton for paraplegic individuals. An analytical solution for the NMPC optimization problem is obtained for small prediction horizons (N < 3 ). Additionally, an iterative solution for longer horizon problems (N >= 3) is performed by employing the linear time-varying approach and using the active set method to include the constraints. Real-time guarantee for the implementation of both NMPC solutions is derived, and the robustness and stability of the closed-loop system are discussed. Finally, the proposed controller is successfully simulated and implemented on a real exoskeleton robot with 1 ms sampling time. The results show that the proposed controller is more effective than PID and adaptive fuzzy controllers.

Automated summary

A nonlinear model predictive controller (NMPC) with input saturation was designed and modified for a rehabilitative exoskeleton, with analytical solutions for small prediction horizons and an iterative solution for longer horizons. Real-time guarantees and successful implementation on a real exoskeleton robot demonstrated the effectiveness of the proposed controller over traditional controllers.