Passive training control for the lower limb rehabilitation robot

It is difficult to obtain an accurate and complete mathematical model for the lower limb rehabilitative robot, and we will do some reasonable approximate treatments when building the model, so the external disturbance, parameter error, unmodeled dynamics and friction are ignored. These reasons will cause poor control performance. The neural network robust control for the lower limb rehabilitation robot based on computed torque method is presented in this paper. The ideal controller according to the Lyapunov stability theories is designed. The ideal dynamic model is controlled by the computed torque method, and RBF neural network controller compensates the unknown uncertainties, then the adaptive robust controller will compensate the approximation error of neural network and the external interference. Therefore the proposed algorithm will improve the system dynamic performance and control accuracy. The controller can guarantee uniformly ultimately bounded. Analysis and the experimental results indict that the proposed algorithm is much more effective and stable than the other control methods when do the passive training.