An observer-based adaptive neural network tracking control of robotic systems
We make a full analysis of the robot states by applying observer-based adaptive wavelet neural network tracking control scheme. The recurrent adaptive wavelet neural network model is used to approximate the dynamics of the robotic system. We use the adaptive wavelet neural dynamics to tune the weights of the robot parameters and the bounds of the gains of delay states. The stability of the closed-loop system is derived from Lyapunov criterion and Riccati-inequality to achieve H? tracking performance. A three-links rolling cart is given to illustrate the effectiveness of the proposed control scheme.
robotic systems lyapunov criterion h? tracking performance adaptive control observer wavelet neural system