Neural Network Control of a Rehabilitation Robot by State and Output Feedback

In this paper, neural network control is presented for a rehabilitation robot with unknown system dynamics. To deal with the system uncertainties and improve the system robustness, adaptive neural networks are used to approximate the unknown model of the robot and adapt interactions between the robot and the patient. Both full state feedback control and output feedback control are considered in this paper. With the proposed control, uniform ultimate boundedness of the closed loop system is achieved in the context of Lyapunov’s stability theory and its associated This work was supported by the National Natural Science Foundation of China under Grant 61203057, the Fundamental Research Funds for the China Central Universities of UESTC under Grant ZYGX2013Z003, and the National Basic Research Program of China (973 Program) under Grant 2014CB744206 W. He ( ) Center for Robotics and School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China e-mail: [email protected] S. S. Ge Department of Electrical & Computer Engineering, National University of Singapore, Downtown Core, 117576 Singapore techniques. The state of the system is proven to converge to a small neighborhood of zero by appropriately choosing design parameters. Extensive simulations for a rehabilitation robot with constraints are carried out to illustrate the effectiveness of the proposed control.