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  • 學位論文

以模糊控制減低高速鐵路橋振動問題和傳統主動控制法之比較

Mitigation of Train Induced Vibration on High Speed Rail Bridges by Fuzzy Control as an Alternative to the Classical Active Control

指導教授 : 段永定

摘要


本研究由傳統主動式結構控制理論必須建立精確的結構數學模型,經由此數學模型來設計控制律。而土木結構系統常為一複雜的多自由度系統,要將結構反應以數學模型精確完整地描述,難度很高,Benchmark 研究計畫的結構數學模型即相當複雜。現代控制理論如模糊控制、調適型控制、可變結構控制及最佳化控制等,以直接狀態回饋控制來解決上述問題,控制律之設計以直接輸出回饋作為依據;其中尤以模糊控制理論(fuzzy control theory),有內在的健實性(inherent robustness),適用於線性與非線性系統。其控制律不須建立精確的結構數學模型,僅依據工程經驗設定控制法則,即可進行控制,非常適合應用於複雜的土木結構控制系統。由於模糊控制法則為工程經驗的定性描述,不須建立系統的數學模型,可減少系統分析和數學模型推導所造成的失真,同時控制過程不須複雜的運算,可降低時間延遲對於控制效果的影響。此外,模糊控制法則具有簡單易懂易修改的特性,於實際應用上更可依據真實控制狀況適時地進行修正調整,接近專家系統,可結合專家之經驗達到控制效果,Benchmark研究計畫的結果即為理想的Knowledge Base。是一種非常簡便有效的控制方法。 本研究將研究以模糊控制減低高速鐵路橋震動問題,並和傳統主動控制法做比較。本研究將設計主動控制器,的主要目的在找適當控制力以減低結構的反應:加速度(Acceleration),速度(Velocity),及位移(Displacement)。它所受到的局限包括控制力的大小(受限於制動器Actuator的特性及所需之能量)及量測位置的數目。以模糊控制處理不確定的輸入值(uncertainties of input data),以語言邏輯來處理複雜的數字模型。需專家系統(Knowledge Base),由於本計畫有Benchmark參照,且根據經驗,任何振動波均可以反相位振動波抵銷,故應可有效設計模糊控制器。模糊控制包含下列步驟: 1. 模糊化之介面:控制器將從感應器量測所得之輸入信號模糊化成語言形式。 2. 使用專家系統(Knowledge Base),包括IF-THEN規則及隸屬函數(Membership function)。 3. 每一規則得出模糊輸出。 4. 解模糊化界面→提供一清楚(Crisp)的輸出控制信號 糊控制器將以SIMULINK(1994)程式及MATLAB(1994)程式所提供之函數來執行。

並列摘要


This research proposal take advantage of the results of a former research project – “Mitigation of Bridge Pier Vibration Induced by High-speed Rail Using Active Control”. It is used as a “Benchmark” project for this proposal. It is described as follows. Significant ground vibrations are often encountered by factories nearby a bridge pier due to a high-speed railway, sometimes causing disruption to production. A “proof of concept” test has been conducted to evaluate the effectiveness of a solution scheme. Several high-speed rail bridge systems have been investigated by researchers to provide field vibration data for the study. A vibrator may be installed at a pier to generate a wave to offset the vibration wave caused by the train propagating through the pier. The activation timing and the magnitude of the artificial vibration has been determined by an active control algorithm in real-time to mitigate the incident wave. This project would include field data analyses, analytical simulations, and small-scale laboratory experimentation in the future. This project was also a joint effort between Structural and Geotechnical, which consists of the following tasks: (1) acquire the horizontal and vertical force and vibration-time histories at the top of the bridge pier; (2) conduct spectral analyses of the time-histories using FFT to determine the frequency compositions; (3) determine the time-histories of the necessary waveform and load magnitudes of the artificial wave to mitigate the incident wave at the top of the bridge pier, based on the theory of active control; (4) construct the finite element models of the high-speed rail systems under investigation; (5) conduct finite element simulations of soil-bridge pier interactions, with and without the mitigating scheme, to determine the effects on the near-field and far-field soil vibration levels. The results have been described in the main text. As of design of an active controller, the goal is the reduction of the structural response in terms of accelerations, velocities and displacements under the limitation of both the control force level (limited by actuators feature and by the required amount of energy) and by the number of measured signals. Fuzzy theory has been recently proposed for the active structural control of civil engineering systems. As an alternative to classical control theory, it allows the resolution of imprecise or uncertain information. Moreover fuzzy control can handle the nonlinear behaviour of structures and avoid establish a complicate mathematical model. The main advantages in adopting a fuzzy control schemes can be summarized as follows. 1. The uncertainties of input data from the ground motion and structural vibrations sensors are treated in a much easier way by fuzzy control theory than by classical control theory. Fuzzy logic, which is the basis of the fuzzy controller, intrinsically accounts for such uncertainties. The implementation of fuzzy controllers makes use of linguistic synthesis and therefore they are not affected by the selection of a specific mathematical model. As a consequence the resulting fuzzy controller possesses an inherent robustness. 2. The whole fuzzy controller can be easily implemented in a fuzzy chip, which guarantees immediate reaction times and autonomous power supply. 3. The knowledge base identifies the actual variables driving the control process: in the specific benchmark problem developed throughout this paper only two variables must be measured and estimated to implement the controller. The benchmark is assuming the linear model is consistent with the real structural system. For a more realistic implementation, at least geometric non-linearities should be incorporated in the problem. The fuzzy controller does not require modifications to follow such a case. Fuzzy control converts a linguistic control strategy into an automatic control strategy. The classic fuzzy inference scheme consists of the following steps: 1. fuzzification interface (the controller input variables, measured from the structure, are fuzzified into linguistic terms), 2. knowledge base (consisting of fuzzy IF-THEN rules and membership functions), 3. fuzzy reasoning (resulting in a fuzzy output for each rule), 4. defuzzification interface (providing the crisp control signal). In this proposal, the preliminary design of the controller will couple the Larsen’s min product rule, to combine the membership values for each rule, with the centre of gravity (COG) defuzzification scheme, to obtain the output crisp value. The controller can also be optimized by the algorithm that uses the Takagi and Sugeno inference system. This computes the fuzzy output for each rule as a linear combination of input variable membership values plus a constant term. The final crisp output is achieved using a weighted average. The fuzzy controller is implemented into the SIMULINK code by two MATLAB functions.

並列關鍵字

Fuzzy control Active Control High-speed Rail

參考文獻


[6] 梁尹齡,高速列車引致之橋墩基礎與地盤振動反應分析,淡江大學土木工程學系,碩士論文,民國94年.
[23]林家銘, 磁流變阻尼器之結構半主動模糊控制, 國立成功大學土木工程研究所, 碩士論文, 民國93年。
[8] 黃昭勝, 結構半主動模糊控制基因演算法之研究, 國立成功大學土木工程研究所, 碩士論文, 民國95年.
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