Precise position control of an ultrasonic motor using the PID controller combined with NN

Abstract

SUMMARYThe input-output characteristic of an ultrasonic motor (USM) has nonlinear elements and changes with a temperature rise or fluctuation of load-mass. Therefore, it is difficult to accomplish satisfactory control performance by using conventional PID controllers. In this paper, we propose a PID controller combined with a neural network (NN-PID controller). In this design method, the controller gains consist of constant gains of the PID controller and variable gains of the NN controller. The weights of the NN are adjusted by the backpropagation method so that the control error can be minimized. This method does not require long learning time of the NN. The effectiveness of the proposed design method is confirmed by experiments using an existing USM.

Precise position control of a travelling wave ultrasonic motor

“…On the other hand, some authors experienced many techniques to achieve robustness in position controls, for example adaptive controls [2], fuzzylogic [3] and neural networks [4] [5] are used to make up for lack of knowledge about the motor and its load. Robustness and precision are thus obtained, but at the expense of tuning complexity, while transient performances -overshoot and response time -are not compared.…”

Improving Model-Free Control Algorithms Based on Data-Driven and Model-Driven Approaches: A Research Study

“…It possesses strong nonlinear modeling capability and robustness, enabling it to achieve superior control performance for unknown system models or changing environments. In [17], the authors proposed an NN PID control scheme for an ultrasonic motor so that the NN can dynamically adjust the control gain to obtain optimal control performance. In [15], an NN PID control system was designed to enhance the efficiency of flying simulator control and achieve a better control performance compared to traditional PID control arithmetic.…”

Precise Position Control of a Traveling-Wave Ultrasonic Motor

“…On the one hand, it has been shown, for example in [1], that to obtain torque at low rotational speed, and thus attain good position error, it is useful to change the temporal phase shift between the two supply voltages. On the other hand, some authors experimented with many techniques to achieve robustness in position controls, for example adaptive controls [2], fuzzy-logic [3] and neural networks [4] [5] are used to make up for lack of knowledge about the motor and its load. Robustness and precision are thus obtained, but at the expense of tuning complexity, while transient performancesovershoot and response time -are not compared.…”