The theoretical development of a trajectory-tracking neural network control
ler based on the theory of continuous sliding-mode controllers is shown in
the paper. Derived equations of the on-line adaptive neural network control
ler were verified on a real industrial direct-drive 3 degrees of freedom (D
.O.F.) PUMA mechanism. The new neural network continuous sliding-mode contr
oller was successfully tested for trajectory-tracking control tasks with re
spect to three criteria: convergence properties of the proposed control alg
orithm (high-speed cyclic movement, low-speed movement, high-speed PTP move
ment), adaptation capability of the algorithm to sudden changes in the mani
pulator dynamics (load), and generalization properties of the proposed cont
rol scheme. An interesting effect of the lower position error after a trans
ient time at sudden load changes is shown. (C) 1998 Elsevier Science Ltd. A
ll rights reserved.