Neural network control for direct-drive robot mechanisms
The theoretical development of a trajectory-tracking neural network controller based on the theory of continuous sliding-mode controllers is shown in the paper. Derived equations of the on-line adaptive neural network controller were verified on a real industrial direct-drive 3 degrees of freedom (D.O.F.) PUMA mechanism. The new neural network continuous sliding-mode controller was successfully tested for trajectory-tracking control tasks with respect to three criteria: convergence properties of the proposed control algorithm (high-speed cyclic movement, low-speed movement, high-speed PTP movement), adaptation capability of the algorithm to sudden changes in the manipulator dynamics (load), and generalization properties of the proposed control scheme. An interesting effect of the lower position error after a transient time at sudden load changes is shown.
direct-drive robots continuous sliding-mode neural network adaptive control applications