Neural network learning rules for control: application to AUV tracking control

Abstract

The authors present two original learning rules for control and compare their performance in the control of an autonomous underwater vehicle. The problem of tracking a reference trajectory with neural controllers is also investigated. The authors discuss the adaptive features of neural networks for control. It is experimentally and theoretically shown that one of the learning rules proposed can perform accurate tracking control in a nonlinear system theory, which explains regulation mechanisms of state-constrained control systems. Numerical results are presented for the tracking control of the dolphin 3 K vehicle.<>