Application of the Rolling Optimization Based on Artificial Immune Algorithm to Rehabilitation Robot

In the process of active rehabilitation training for an upper limb rehabilitation robot, unpredictable interaction forces exist between human and robot, and the disturbance of patients' spasm is an issue. To address these problems, this paper designed a nonlinear rolling horizon tracking control algorithm based on real-time acquisition of human-robot interaction force. The stability of the controller is analyzed; this controller can predict the future dynamic of the system on the basis of a linear model at each sampling. The artificial immune optimization algorithm is adopted as a rolling optimization strategy, which not only improves the anti-jamming performance of the system but also ensures that the system can obtain a feasible solution in the entire prediction time domain. Simulation results show the effectiveness of the proposed controller.