基於模型參考的分散式自適應觀察控制被由N個組成的一個連續時間的大規模多變系統提出的子系統與未知參數相互連接。使用進化論演算法去找尋最佳的補償項使模型系統能追蹤的更完美，而類比控制的增益是由使用模式參考自適應控制理論基於Lyapunov的方法得到。在這文章內，它顯示了取樣分散式自適應控制理論能接近我們設計的輸出使誤差接近零。子系統之控制器無法取得其他子系統之資訊此一限制形成本研究之先天條件。而該架構之本質，控制系統之設計可拆分為兩部分，一是使模型狀態緊隨參考模型，另一部分是使模型訊號狀態緊隨參考訊號。理想的數位再設計系統對於數位分散式適應控制被提出討論。

A novel model-reference-based decentralized adaptive observer-based controller is proposed for a continuous-time large scale multivariable system consisting of N interconnected linear subsystem with unknown parameters. It is perfect to use evolutionism programming algorithms to look for the best parameter of model system. The adaptation of the analog controller gain is derived by using model-reference adaptive control theory based on Lyapunov's method. In this thesis, it is shown that the sampled-data decentralized adaptive control systems theoretically possible to asymptotically track desired outputs with zero error. We make the state of the model follow the model of consulting closely, another part makes the signal state of the model follow the signal of the plant closely. The optimal digital redesigned controller for the sampled-data decentralized adaptive control systems is newly proposed.

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