Direct MRAC with dynamically constructed neural controllers

Research in neural control mostly concentrates on indirect control schemes while insufficient attention has been paid to direct model reference adaptive control (MRAC) scheme. In addition, at present the emphasis of neural control is on parameter tuning instead of structural tuning, i.e., to find the minimal controller capable of achieving an optimal performance. The stability of the neural control schemes (i.e. the requirement of persistency of excitation and bounded learning rates) also requires more attention. Furthermore, localized architectures are needed in order to deal with the moving target problem (i.e. the difficulty for global neural networks to perform several separate computational tasks in closed-loop control). The purpose of the present paper is to show that direct MRAC using dynamically constructed neural controllers, such as the fuzzy neural and the cascade correlation, satisfy above requirements and offers a method for automatic discovery of an efficient controller.