An immune feedback mechanism based adaptive learning of neural network controller

Abstract

Both neural networks and immunity-based systems are biologically inspired techniques that have the capability of identifying and controlling. The information processing principles of these natural systems inspired the development of intelligent problem solving techniques, namely, the artificial neural network and the artificial immune system. An adaptive learning method for a neural network (NN) controller using an immune feedback law is proposed. The immune feedback law features rapid response to foreign matter and rapid stabilization of biological immune systems. Several improvements can be made to improve gradient descent NN learning algorithms. The use of an adaptive learning rate attempts to keep the learning step size as large as possible while keeping learning stable. In the proposed method, because the immune feedback law changes the learning rate of the NN individually and adaptively, it is expected that a cost function is rapidly minimized and learning time is decreased. In the control structure, a reference signal self-organizing control system using NNs for flexible microactuators is used. In this system, the NN functions as a reference input filter, setting new reference signals in the closed loop system. Numerical and experimental results show that the proposed control system is effective in tracking a reference signal.