Servo position control of ultrasonic motors using fuzzy neural network

With their significant advantages over traditional electromagnetic motors, ultrasonic motors (USMs) are becoming attractive for mechatronic applications. Since USMs suffer from a lack of applicable mathematical model while their speed characteristics are heavily nonlinear and time varying, it used to be difficult to apply them for servo application. This paper presents a fuzzy ne ural network controller for servo position control of an USM. It combines both the knowledge-based fuzzy logic and the learning-incorporated neural network. As a result, it compensates the nonlinear behavior of the motor and optimizes its performance on-line. To further improve the motor performance, both of their control inputs, namely the driving frequency and phase difference of the 2-phase inverter waveforms, are employed. Experiments are then performed for various reference inputs. The results demonstrate superiority of the controller in terms of tracking and steady-state performance.