Neuro-optimal controller for robot manipulators

Abstract

This paper presents an algorithm for continuous-time quadratic optimization with neural compensation of motion control. A simpler reformulation explicit solution to the Hamilton-Jacobi-Bellman equation for optimal control of rigid robot motion is found by solving an algebraic Riccati matrix equation. The system stability is investigated according to Lyapunov function theory and it is shown that global asymptotic stability holds in the case of known system model. It is also shown how optimal control and neural control may act in concert in the case of unknown system model. The neural algorithm is derived from Lyapunov stability analysis, so that both system-tracking stability and error convergence can be guaranteed in the closed-loop system. Experimental and simulation results from a two-link robot manipulator show the satisfactory performance of the proposed control scheme. © 2013 Springer-Verlag Berlin Heidelberg.