Adaptive control for vibration suppression by using self-organization map

Abstract

An adaptive control method using a self-organizing map (SOM) which is a kind of neural networks is proposed to suppress vibration of smart structures. The SOM learns characteristics of given data without supervising and categorizes high dimensional data into low dimensional maps with keeping complex relationships among data. The present method employs the SOM to estimate states of the controlled object and makes a lookup table of control input. Each node of the state estimation SOM consists of vectors including controlled responses and control input. The SOM and lookup table learn and are updated only when the effective control input is applied to the controlled object where the evaluation function of control system is defined by differences between the desired state and current state. The present method just requires information about the control response and input, resulting in implementation of the vibration control without numerical models for controlled object. Numerical and experimental results are given for the smart structure fabricated by an aluminum plate and piezoelectric actuators, assuming as an unknown object. Both numerical and experimental results successfully demonstrate effectiveness of the present method.