Robust learning control using universal learning network

Characteristics of control system design using Universal Learning Network(U.L.N.) are that a system to be controlled and a controller are both constructed by U.L.N. and that the controller is best tuned through learning. U.L.N. has the same generalization ability as N.N.. So the controller constructed by U.L.N. is able to control the system in a favorable 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 U.L.N. and second order derivatives of U.L.N.. The proposed method can realize better performance and robustness than the commonly used Neural Network. Robust learning control considered here is defined as follows. Even though initial values of node outputs change from those at learning, the control system is able to reduce its influence to other node outputs and can control the system in a preferable way as in the case of no variation.