| Summary: | 博士 === 國立中興大學 === 電機工程學系所 === 104 === This dissertation presents several adaptive predictive control methods of a class of nonlinear discrete-time time-delay systems not only for guaranteed stability but also for precise setpoint tracking and disturbance rejection. To synthesize these overall adaptive predictive control systems, the research topics of the dissertation are classified into four core techniques. First, an adaptive stable generalized predictive control with fuzzy modeling (FASGPC) is proposed for combining the fuzzy modeling method and stable generalized predictive control (SGPC) strategy. Second, an intelligent adaptive two-degrees-of-freedom control is presented for combining a Takagi-Sugeno-Kang (TSK) type recurrent fuzzy neural network (TRFNN) adaptive inverse model feedforward controller with a stochastic adaptive model reference predictive controller (SAMRPC). Third, an adaptive predictive proportional-integral-derivative (PID) control approach by utilizing recurrent wavelet neural networks (RWNN-APPID) is derived and examined by the recurrent wavelet neural networks (RWNN) identifier, an adjusting mechanism and an adaptive predictive PID controller. Fourth, an adaptive predictive PID control via fuzzy wavelet neural networks (FWNN-APPID) is established by utilizing the fuzzy wavelet neural networks (FWNN) to tune PID parameters such that the proposed adaptive predictive PID control (FWNN-APPID) is capable of providing satisfactory control performance for a class of highly nonlinear discrete-time systems with time-delay. The effectiveness and merits of all the proposed methods are well exemplified by conducting several simulations on many nonlinear discrete-time time-delay systems.
|
|---|
