由於現行的工業冷凍系統的控制方法有較耗電的缺點，先進控制器的設計方法為將冷凍系統的控制描述為一個模式預測控制問題，目標函數為修正的平均COP值，由於此最佳化問題求解的複雜度，通常設計搜尋演算法來求得最佳控制決策的近似解，已經發展的搜尋演算法有“枝界法”和“離線訓練的類神經動態規劃法”，模擬結果顯示這些控制方法能得到較高的平均COP值和持續較低的動力消耗，能達到省能的目的。本文進一步發展“線上訓練的類神經動態規劃法”，本文更考慮冷凍系統模式和實際系統之間存在差異性的問題，模式的不確定性可能影響控制的性能，一個實用的最佳控制策略的性能必須對不易精確設定的模式參數的誤差具有強健性，本文對此進行敏感度分析。

The currently used control schemes of industrial refrigeration systems have the drawback of more energy-consuming. More advanced control schemes were designed by formulating the control of refrigeration systems as a model predictive control problem. The objective function is a modified time-averaged COP. Because of the complexity of the optimization problem, search algorithms were developed for searching the approximate optimal control decisions. The control schemes based on “branch and bound” and “off-line learning neuro-dynamic programming” search algorithms have been developed in previous studies. The simulation results showed that the proposed control schemes achieved higher time-averaged COP and lower power consumption. In this thesis, an “on-line learning neuro-dynamic programming” control scheme is developed. Furthermore, the difference between the predictive model and the refrigeration plant is considered. The performance of the control schemes may be degraded by model uncertainty. The robustness of the performance of control schemes to model uncertainty is investigated by sensitivity analysis。

誌謝 i
中文摘要 ii
Abstract iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章 緒論 1
1.1 前言 1
1.2 文獻回顧 2
1.3 研究目的 3
第二章 冷藏倉庫冷凍系統及控制 4
2.1 冷凍原理簡介 4
2.2 冷藏倉庫冷凍系統和控制方法 5
2.3 業界控制方法的困難點 8
第三章 冷凍系統最佳化操作模式 9
3.1 冷凍系統最佳化操作模式 9
3.2 模式預測控制(Model Predictive Control) 13
第四章 最佳化操作問題解的搜尋法 15
4.1 枝界法搜尋演算法 15
4.1.1 枝界法-13搜尋演算法 16
4.1.2 枝界法-169搜尋演算法 17
4.2 類神經動態規劃法 19
4.3 類神經網路之架構與訓練 20
第五章 模擬結果 24
5.1 控制方法的性能比較 24
5.1.1 模擬ㄧ 24
5.1.2 模擬二 27
5.1.3 模擬三 30
5.1.4 模擬四 33
5.1.5 控制性能模擬結果討論 36
5.2 預測時段 之選定 37
5.3 初始條件對枝界法性能的影響 41
5.4 切換損失係數之敏感度(sensitivity)分析 42
5.4.1 模擬ㄧ 42
5.4.2 模擬二 45
5.4.3 模擬三 47
5.4.4 模擬四 49
5.5 冷藏室溫度變化模式之敏感度分析 51
5.6 敏感度分析結果討論 52
第六章 結論 53
參考文獻 55

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