NEURAL NETWORK CONTROL OF UNCERTAINTY ROBOTIC MANIPULATOR WITH H^∞ TRACKING PERFORMANCE

In this paper, a Neural Network control with H^∞ tracking performance for uncertainty robotic manipulator is proposed. The control scheme combined H^∞ control theory and NN adptive algorithm organically. If the weight to control variables is appropriatly chosen, the influence of both NN aproximation error and external disturbance can be attenuated to a desired level. Based on Lyapunov method, NN learning law is given and H^∞ tracking performance is illustrated. Finally, the developed controller is applied to a two-link robotic manipulator. Simulation results demonstrate that the control scheme is effective.