Supervisory control for linear piezoelectric ceramic motor drive using genetic algorithm

Abstract

This paper presents a supervisory genetic algorithm (SGA) control system for a linear piezoelectric ceramic motor (LPCM) driven by a two-inductance two-capacitance resonant driving circuit. First, the motor configuration and driving circuit of an LPCM are introduced, and its hypothetical dynamic model is described briefly. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an SGA control system is therefore investigated to achieve high-precision position control. The proposed SGA control system is composed of two parts. One is a GA control that is utilized to search an optimum control effort online via gradient descent training process, and the other is a supervisory control to stabilize the system states around a predefined bound region. Compared with conventional GA control systems, the proposed control scheme possesses the salient advantages of simple structure, fewer executing time, and good self-organizing properties. The effectiveness of the proposed driving circuit and control system is verified with numerical simulations and hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a traditional integral-proportional position-control system.