Robust learning control using universal learning network

Abstract

Characteristics of control system design using universal learning network (ULN) are that a system to be controlled and a controller are both constructed by ULN, and that the controller is best tuned through learning. ULN has the same generalization ability as a neural network (NN). Thus the controller constructed by ULN is able to control the system in a favourable way under the condition different from the condition of the control system at learning stage, but stability can not be realized sufficiently. In this paper, we propose a robust learning control method using ULN and second order derivatives of ULN. The proposed method can realize better performance and robustness than the commonly used NN. The robust learning control considered here is defined as follows: even though the initial values of the node outputs change from those at learning, the control system is able to reduce its influence on other node outputs and can control the system in a preferable way as in the case of no variation.