Optimal neural control for constrained robotic manipulators

In this paper, a neural network control based on optimal quadratic regulators is developed for the stabilization of constrained nonlinear robotic systems. This method of robotic control is performed by adding an optimal control, generated from the dynamics of the position error, to a neural control, estimated through a three layers neural network. Solving an algebraic Riccati equation, solutions of the Hamilton Jacobi Bellman (HJB) equation are found for the dynamic error optimal control. The adaptation algorithm of the neural control is derived from the Lyapunov analysis for the robotic system overall stability. The simulations results of a six degrees of freedom arm manipulator, Puma 560, with this neural optimal control, are presented to validate the proposed approach. Global stability, of the constrained robot, is assured through the developed controller, in the presence of uncertain gravity vector, viscous and Coulomb's friction and external disturbances.