模糊遺傳系統馬達之PWM控制

Abstract

本論文主要是利用遺傳演算法調整模糊系統參數以控制不確定之非線性系統。透過遺傳演算法線上即時調整模糊系統之參數，以產生適應性模糊系統控制器。為了能夠在線上調整這些參數，透過評估的閉迴路系統的穩定性作為遺傳演算法的適應性函數。應用本文先藉由李亞普諾夫函數分析系統穩定性，然後設計硬體電路實現於具有直流電轉換器之馬達控制，其控制方法可分為雙迴路控制與適應性遺傳倒階控制。雙迴路控制包括遺傳模糊系統迴路與PID控制器迴路；而適應性遺傳倒階控制，主要利用是倒階控制技術與遺傳模糊系統近似特性。實驗結果展現良好追蹤成效與效能。

In this thesis, a genetic algorithm is used to tune the parameters of the fuzzy system for nonlinear system control. The parameters of the fuzzy system are online adjusted by the genetic algorithm in order to generate appropriate control input. For the purpose of on-line evaluating the stability of the closed-loop system, an energy fitness function derived from backstepping technique is involved in the genetic algorithm. First, the stability of the closed-loop system is verified by using Lyapunov function, and then the hardware control circuits are designed for motor systems with DC-DC buck converter. Based on double-loop control and adaptive genetic backstepping control, some experiments are performed for the motor systems with DC-DC buck converter. The double-loop control involves genetic fuzzy control loop and PID control loop, and the adaptive genetic backstepping control uses backstepping technique and the approximation ability of the genetic fuzzy system. According to these experimental results, the double-loop control and adaptive genetic backstepping control scheme can perform on-line tracking successfully.