| Summary: | 碩士 === 國立高雄第一科技大學 === 機械與自動化工程所 === 90 ===
In this thesis, the grey-fuzzy gain scheduling (GFGS) PID control scheme is proposed for autopilot system. The GFGS PID control scheme consists of two parts: the grey predictor and the fuzzy gain scheduling (FGS) PID controller. The difference between GFGS PID controller and FGS PID controller is that the GFGS PID controller is designed based on the future state, which is predicted by the grey model. Thus there will be the advantage of prior control. In real engineering problems, we usually face the multiple-objectives optimization problems. Therefore, in order to search for the optimal control parameters by way of systematic reasoning instead of the time-consuming trial-and-error procedure. An optimal combined method, i.e., robust multi-criteria optimization (RMCO) approach, is applied in this thesis to search for the optimal control parameters of both the grey predictor and the fuzzy gain scheduling PID controller (i.e., the sample size of the grey predictor, the scaling factors of the fuzzy PID controller and factors of sigmoid membership functions) for ensuring both stability and control performances. RMCO approach integrates multi-objective optimization concepts with statistical Taguchi method and obtains a Pareto-optimal robust design solution set with the aid of design of experiment set-ups, which uses analysis of variance (ANOVA) results to quantify relative significance of design factors. Computer simulations of the course-keeping autopilot are performed to verify the effectiveness of the RMCO-GFGS PID control scheme and to show that the GFGS PID control scheme is superior to the existing FGS PID control scheme.
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