<title>Fussy neural-network-based control system for active damping of slewing structures with piezoceramic actuators</title>

Abstract

This paper presents a control scheme based on fuzzy logic and neural network algorithms and its application to vibration damping of slewing flexible structures with piezoceramic sensors and actuators. In particular, the algorithms are implemented on a flexible-link manipulator. Here, a fuzzy-based adaptive controller is considered due to its simplicity and the fact that it does not require expressing the controller in terms of the system parameters, as is necessary in the case of self-tuning regulators. This controller is based on a functional fuzzy model where the consequents are crisp functions of the system states and/or inputs; these functions represent controllers designed for different operating regimes. The premise is constituted by the fuzzy subsets corresponding to the process parameters estimated in real time. The neural network is utilized for two different purposes: (1) derivation of a fuzzy model of the system (fuzzy system identification), and (2) derivation of the rule base for the fuzzy logic controller through training. The effectiveness of this new scheme is verified on a clamped-free beam and a flexible-link manipulator instrumented with piezoceramic sensors and actuators. It will be shown that robust performance may be achieved in face of large parameter variations.