The ultrasonic motor (USM) has a heavy nonlinearity and time-varying characteristics which vary with driving conditions.Because of no-exact mathematical model of USM, it is difficult to control USM. PID controller can be designed without using the expression model of plant, but it is hard to compensate the nonlinearity and characteristic changes of USM. This paper presents a self-tuning scheme using a modified particle swarm optimization (MPSO) for PID controller to overcome the dynamic characteristics of USM. A modified PSO employs the strategy that nonlinearity decreases the value of inertia weight from a large value to a small value. This strategy is to improve the performance of the standard PSO in global search and fine-tuning of the solution. The effectiveness of the proposed method is verified by numerical simulation and experimental investigation.The results demonstrate that the proposed method can improve the accuracy of USM.
In order to get good performance of ultrasonic motors (USMs) in real applications, a real-time intelligent PID controller is proposed in this paper. To overcome the problems of characteristic variation and nonlinearity, an intelligent PID controller combined with particle swarm optimization (PSO) type neural network (NN) is studied in real-time environment for USM control. In the proposed method, an NN controller is designed for adjusting PID gains. The learning of NN is implemented by PSO updating the weights of NN on-line. By employing the proposed method, the characteristic changes and nonlinearity of USM can be compensated effectively in real-time environment. The effectiveness of the method is confirmed by experiments.
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