Speed control of ultrasonic motors using neural network

The ultrasonic motor (USM) is a newly developed motor, and it has excellent performance and many useful features, therefore, it has been expected to be of practical use. However, the driving principle of USM is different from that of other electromagnetic-type motors, and the mathematical model is complex to apply to motor control. Furthermore, the speed characteristics of the motor have heavy nonlinearity and vary with driving conditions. Hence, the precise speed control of USM is generally difficult. This paper proposes a new speed-control scheme for USM using a neural network. The proposed controller can approximate the nonlinear input-output mappings of the motor using a neural network and can compensate the characteristic variations by on-line learning using the error backpropagation algorithm. Then, the trained network finally makes an inverse model of the motor. The usefulness and validity of the proposed control scheme are examined in experiments.