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研究生： 鄺智傑
Kuang, Chi-Chieh
論文名稱： 適用於非線性隨機混合系統的嶄新ARMAX模型 狀態空間自調式控制
Novel ARMAX Model for State-Space Self-Tuning Control of Nonlinear Stochastic Hybrid Systems
指導教授： 蔡聖鴻
Tsai, S. H. Jason
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2006
畢業學年度： 94
語文別： 英文
論文頁數： 61
中文關鍵詞： 自調式控制 、非線性隨機系統 、NARMAX模型
外文關鍵詞： NARMAX model, self-tuning control, nonlinear stochastic system
相關次數： 點閱：84  下載：2 
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    • 本論文提出了一個針對非線性隨機混合系統的嶄新狀態空間自適應控制方法。透過最佳線性化法則，我們可以利用NARMA基礎的模型來建構出非線性隨機狀態空間自調式控制系統。此外，我們提出一種對具有多變數非線性複雜系統自調式的適當數位控制器設計方法，其中系統參數未知、存在脈衝模點、有可量測的雜訊、決定性的雜訊和不可得知的狀態。本文提出的方法使得對於非線性隨機混合系統的進階數位控制演算法更具發展性。

    A novel state-space self-tuning control methodology for a nonlinear stochastic hybrid system with stochastic/deterministic noise is proposed in this thesis. Via the optimal linearization approach, an adjustable NARMA-based noise model with estimated states can be constructed for the state-space self-tuning control in nonlinear continuous-time stochastic systems. In addition, a state-space self-tuning control scheme for the adaptive digital control of continuous-time multivariable nonlinear stochastic systems, which have unknown system parameters, measurement noise, deterministic noise, and inaccessible system states, is proposed. The proposed method enables the development of digitally implemental advanced control algorithms for nonlinear stochastic hybrid systems.

    Page 摘要 I Abstract II Acknowledgments III List of figures V Chapter 1 Introduction 1-1 2 NARMAX Model for State-Space Self-Tuning Control 2-1 2.1 The prediction-based digital controller design 2-2 2.2 NARMAX model for self-tuning control scheme of SISO case 2-3 2.3 NARMAX model for self-tuning control scheme of MIMO case 2-5 2.4 Preliminary structures of discrete-time state-space observer 2-6 2.5 The method of optimal linearization 2-9 2.6 The combination of discrete-time state-space observer and STC scheme with NARMAX model through optimal linear model 2-13 3 Design of a State-Space Self-Tuning Controller with NARMAX Model 3-1 3.1 The structure of state-space self-tuner with NARMAX model 3-2 3.2 Example 3.1 (one-input-one-output) 3-7 3.3 Example 3.2 (two-inputs-two-outputs) 3-12 3.4 Example 3.3 (two-inputs-four-outputs) 3-19 4 Conclusions 4-1 References

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