| Summary: | 博士 === 國立東華大學 === 電機工程學系 === 94 === The subject of this dissertation is to develop a precise position control system for piezoelectric actuators using intelligent control. First, a new hysteresis friction model is proposed. Then, by integrating the hysteresis friction model, the overall motion dynamics of the one-dimensional and the two-dimensional piezoelectric actuators are established. Moreover, the control performance of the two-dimensional piezoelectric actuator is always deteriorated due to the cross-coupling effect. Therefore, the cross-coupling effect is also included in the motion dynamic of the two-dimensional piezoelectric actuator. Furthermore, an adaptive recurrent fuzzy neural network (RFNN) control system, an adaptive wavelet neural network (WNN) control system and an adaptive recurrent radial basis function network (RRBFN) control system are developed individually for the precise position control with robustness of the one-dimensional and the two-dimensional piezoelectric actuator. In the adaptive RFNN control system of the one-dimensional piezoelectric actuator, an adaptive control with hysteresis estimation and compensation is proposed. However, in the designed adaptive controller, the lumped uncertainty is difficult to obtain in practical application. Therefore, a RFNN is adopted as an uncertainty observer in order to adapt the value of the lumped uncertainty on line. In the adaptive WNN control system of the one-dimensional piezoelectric actuator, a WNN with accurate approximation capability is employed to approximate the part of the unknown function in the dynamics of the one-dimensional piezoelectric actuator, and a robust compensator is proposed to confront the lumped uncertainty that comprises the inevitable approximation errors due to approximation error, optimal parameter vectors, and higher-order terms in Taylor series. An adaptive law is investigated to estimate the lumped uncertainty in the robust controller, and adaptive learning algorithms for the on-line learning of the parameters of the WNN are derived based on the Lyapunov stability theorem. In the adaptive RRBFN control system of the two-dimensional piezoelectric actuator, a RRBFN with accurate approximation capability, simple architecture, and fast learning is employed to approximate an unknown dynamic function in the dynamics of the two-dimensional piezoelectric actuator, and the adaptive learning algorithms that can learn the parameters of the RRBFN on line are derived using Lyapunov stability theorem. A robust controller is proposed to confront the uncertainties including approximation error, optimal parameter vectors, higher-order terms in Taylor series. To relax the requirement of the value of the lumped uncertainty in the robust controller, an adaptive law is investigated to estimate the lumped uncertainty. In the proposed intelligent control systems, the neural network control theorems with accurate approximation capability are employed to approximate the nonlinear function of the motion dynamic model. In addition, the parameters of the intelligent control systems are learning on line. Using the proposed control systems, the robustness to hysteresis, cross-coupling effects, parameter variations and external disturbances can be obtained. Finally, the effectiveness of the proposed control schemes are demonstrated by some experimental results.
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