The paper is concerned with the application of wavelet-based neural networks for optimal control of robotic manipulators motion. The model of robotic manipulators with regard to frictions and disturbances is nonlinear and uncertain. Optimal control law is found by the optimization of the Hamilton–Jacobi–Bellman (H-J-B) equation and it shows how wavelet-based neural networks can overcome nonlinearities through optimization without preliminary off-line learning phase. The neural network is learned as on-line and an adaptive learning algorithm is derived from the Lyapunov theory. This is so that both tracking stability and error convergence of the estimation for the nonlinear function can be guaranteed in the closed-loop system. The Lyapunov function for the nonlinear analysis is derived from the user input in terms of a specified quadratic performance index. Simulation results on a three-link robot manipulator show the satisfactory performance of the proposed control schemes even in the presence of large modeling uncertainties and external disturbances. Furthermore, it is shown that the tracking error for wavelet neural networks is less than conventional neural networks.
Discussion(0)
No comments yet. Be the first to comment.