Wavelet-based intelligent optimal control of robotic manipulators

The paper is concerned with the application of wavelet-based neural networks for optimal control of robotic manipulators motion. Optimal control law is found by optimization of Hamilton-Jacobi-Bellman (H-J-B) equation and it is shown how wavelet-based neural networks can overcome nonlinearities through optimization with no preliminary off-line learning phase required. The neural network is learned as on-line and the adaptive learning algorithm is derived from Lyapunov stability analysis. So that both system tracking stability and error convergence of nonlinear function estimating 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 illustrate the effectiveness of our method.