動態類神經網路控制系統之設計及其應用

Abstract

由於控制器的老化而造成控制系統的出錯是很普遍的，而這種情況發生，常常因為某些原因，原本的控制器很難被修復。本篇論文探討以動態類神經網路控制器取代原本控制器的可行性來設法解決上述問題。我們以霍普菲爾類神經網路控制器做為動態類神經網路控制器。先以最陡坡降演算法離線訓練霍普菲爾類神經網路的網路權重值使得霍普菲爾類神經網路的輸出能模仿原先的控制器。訓練完成之後再將該霍普菲爾類神經網路當作控制系統的即時控制器。我們以倒單擺系統及球桿系統來驗證該霍普菲爾類神經網路控制器的效果。模擬的結果顯示即使控制系統在和訓練時有不同的初始條件，該霍普菲爾類神經網路控制器依然可以模仿原先的控制器並達到令人滿意的效能。

Faults due to the aging of a controller for a control system are very common; once they happen, the controller is quite difficult to be repaired for some reasons. To solve this problem, in this thesis, we discuss the feasibility of replacing the existing controller with a dynamical neural network (DNN) controller. A Hopfield neural network (HNN) controller is used as the DNN controller. The weightings of the HNN are first trained off line by the steepest descent algorithm to make the output of the HNN can mimic the existing controller. After the training is completed, the HNN is applied to the control system as a real-time controller. An inverted pendulum system (IPS) and a ball and beam system (BABS) are used to examine the effectiveness of the proposed HNN controller. The simulation results show that even with the initial condition different from that in the training data, the proposed HNN controller can mimic the existing controller and achieve favorable performance.