Neural Network Based Modeling and Predictive Position Control of Traveling Wave Ultrasonic Motor Using Chaotic Genetic Algorithm

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Traveling wave ultrasonic motors (TWUSMs) possess heavy nonlinearities and load-dependent characteristics such as dead-zone. Therefore, modeling and controlling of TWUSMs are difficult and challenging tasks. In this paper, a Hammerstein model is proposed for rotary TWUSM (RTWUSM) which is proper for control purposes. This model is constructed using the chaotic genetic algorithm (CGA) and radial basis function neural network (RBFNN). Then, Model Predictive Controller (MPC) with online CGA optimizer is applied based on the presented model. Simulation results and their validation with the data derived from experiments demonstrate the effectiveness of the proposed model and controller.
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Traveling Wave Ultrasonic Motor; Model Predictive Control; Chaos Theory; Genetic Algorithm; Neural Network

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