Genetic Algorithm Based Dynamics Modeling and Control of a Parallel Rehabilitation Robot

Abstract

This paper is devoted to modeling and controlling a new upper-limb rehabilitation robot which has a parallel structure. Genetic algorithm (GA) is successfully applied in parameter identification based on dynamic analysis of the parallel robot. For accurate identification, joint velocities and accelerations are computed by the Kalman filter. By taking the non-linear characteristics of frictions into account, the unknown friction parameters differ depending on directions of motion. Compared with the traditional least square estimation (LSE) based method, the proposed identification method improves performance. Further, a model-based PD computed-torque controller is designed, and the feasibility of the estimated dynamic model and controller is validated by passive training task along a circular trajectory.