Hybrid Adaptive Control Scheme For Ultrasonic Motors

An ultrasonic motor (USM) is a newly developed motor, which has some excellent performances and useful features. Owing to the advantages mentioned above, the USM has been used in many practical applications. The operational characteristics of the USM depend on many factors, and the study on the model relates to many subjects. It is therefore difficult to construct a precise model of the USM.In recent years, some models of the USM have been proposed, but up to now, the detailed description of the contact process between the stator and rotor is still unsolved. The assumption that the surfaces of the contacting bodies are smooth and that a homogeneous continuum model could describe the contact layer are different from the practical operations. Furthermore the temperature of the stator will be increased along with the long time driven by power, the resonant frequency of the USM will be shifted. There are no good analysis methods to study the influence of the temperature variation on the USM. It is still a challenge to construct a suitable theoretical model involving the temperature effect.The operation of ultrasonic motor is influenced by many factors, strongly nonlinear characteristics could be caused by the increase of temperature, the changes of load, driving frequency and voltage and many other factors. Recently the artificial intelligent methods based on neural networks and fuzzy systems have become main approaches to perform USM control. Using these intelligent control methods, one has achieved good control effectiveness for various kinds of ultrasonic motors. However, the existing intelligent methods for the USM control have some shortcomings, such as complex structures, slower convergent speeds and lower convergent precision, no theoretical guarantee on convergence and stability of control, and some others. Solving these problems is the key to improve the control efficiency of the USM.According to the research progress of modeling and control of ultrasonic motors, this paper (1) give a detailed introduction for the basic concepts, history, models, drive, and control of ultrasonic motors; (2) give a detailed description for the identification and control of nonlinear systems based on neural networks, and know the basic methods and some problems which will be solved for neural networks; (3) proposed a hybrid adaptive speed control scheme for ultrasonic motor. On the basis of a bimodal neural network, a robust controller is introduced, which accelerates the convergence of the system at the initial stage, reduces the sensitivity to system parameter variations and external disturbance. An iterative controller is also introduced to guarantee the proper control input to be provided in each sampling interval, which renders the output of the USM to trace the designated value with acceptable precision. In order to guarantee the convergence and fast training for the control system, the adaptive learning rates of the neural network identifier and iterative controller are derived through the discrete-type Lyapunov stability analysis. Numerical results show that good control effectiveness can be obtained when applying the proposed control scheme at various reference speeds. Both convergent time and precision obtained using the proposed method are obviously superior to those obtained using usual neural network controllers.Numerical experiments on the working conditions of the USM subject to external disturbance, system parameter variations and discontinuously desired output demonstrate that the proposed control scheme has fairly good adaptive performance. These works provide a useful basis for the further study on the USM and intelligent control.