Improving Neural Network based Vibration Control for Smart Structures by Adding Repetitive Control

Neural networks (NN) has been a popular vibration control method because of its robustness and practicability to reject broad band disturbances for complex systems such as smart structures. However, the benign characteristic of NN, suppressing a wide range frequency of disturbances, may also limit its control performance at specific frequencies and inevitably cause non-minimum output responses in particular under persistent excitation. To alleviate this limitation and improve the performance of NN based control methods, this paper presents a hybrid control strategy comprising a neural controller and a repetitive controller for active vibration control of smart structures. The neural controller is a fundamental controller which applies back-propagation networks for performance evaluation. To add repetitive control into the existing NN control system, the work transforms a feedback controller to a feedforward control problem with the solutions of a bezout identity embedded with known internal models of injecting disturbances. The presented hybrid control provides a synergetic effect and aims for better suppression performance subject to complicated disturbances in stringent environments. Experimental results on a flexible beam demonstrate the effectiveness of the proposed control method.