【正文】 問題需要研究。 （3）分析非線性系統(tǒng)的穩(wěn)定性最常用的方法是Lyapunov穩(wěn)定性理論，因而，要改善現(xiàn)有的 TS 模糊系統(tǒng)穩(wěn)定性分析的保守性，在如何設(shè)計(jì)新的 Lyapunov 函數(shù)方面仍有許多工作要做。 （4）雖然本文對(duì)所提出的控制方法都進(jìn)行了仿真驗(yàn)證，但還沒有經(jīng)過實(shí)際工程實(shí)踐的檢驗(yàn)。如何將本文的研究成果應(yīng)用于實(shí)際工程，仍需要大量的研究工作。 雖然模糊控制系統(tǒng)仍然存在很多的問題，但隨著模糊理論研究的不斷深入，模糊系統(tǒng)穩(wěn)定性分析與綜合必將達(dá)到一個(gè)更高的水平。模糊邏輯系統(tǒng)逼近性能也是需要深入研究的問題。模糊系統(tǒng)的萬能逼近性定理，可以被稱作模糊建模與控制的理論基石。模糊系統(tǒng)的逼近性能最常規(guī)的理解是基于誤差的逼近性能，很多種模糊模型被證明具有萬能逼近性，但如何構(gòu)造具有萬能逼近性的模糊系統(tǒng)一直是眾多學(xué)者研究的問題。模糊系統(tǒng)的逼近性能的另一個(gè)重要研究方向是對(duì)模糊系統(tǒng)非線性變化能力的研究。如何定義描述模糊系統(tǒng)的非線性變化能力，如何用最少的參數(shù)獲得最大的非線性變化能力，這些都是非常有研究價(jià)值的問題。謝 辭值此畢業(yè)學(xué)位論文即將完成之際，謹(jǐn)向我的導(dǎo)師張果老師表示深深的敬意和衷心的感謝!是她在論文各階段給予本人富有啟發(fā)性的建議和指導(dǎo)，使本人的論文撰寫工作得以順利的進(jìn)行。導(dǎo)師淵博的學(xué)識(shí)、嚴(yán)謹(jǐn)?shù)闹螌W(xué)態(tài)度、敏銳的洞察力、活躍進(jìn)取的創(chuàng)新意識(shí)、勤懇的工作作風(fēng)和平易近人的長者風(fēng)范將影響和激勵(lì)我的一生。導(dǎo)師對(duì)我在學(xué)習(xí)，工作和生活等各個(gè)方面的關(guān)心和支持，學(xué)生將銘記在心！本科學(xué)習(xí)期間，電氣工程與自動(dòng)化系的良好學(xué)術(shù)氛圍使我獲益匪淺．感謝自動(dòng)化系的老師們，他們講授的課程讓我在自動(dòng)化方面得到了很大的提高。同時(shí)要感謝所有自動(dòng)化的老師，在這期間給予我學(xué)習(xí)上的鼓勵(lì)和生活上的關(guān)心．感謝同宿舍同班以及共同做畢業(yè)設(shè)計(jì)的兄弟姐妹平日里在學(xué)習(xí)和生活上的交流與幫助，理論學(xué)習(xí)上的合作與探討使我不斷取得進(jìn)步，倍感朋友的友情。特別感謝舍友為本文提出了寶貴意見。感謝我辛勤勞作的父母和親人，在漫長的求學(xué)生涯中，是他們始終如一地給予我理解、關(guān)心和支持，使我能夠順利完成論文。***2011年5月于璁苑參考文獻(xiàn)[1] 李士勇. 模糊控制,神經(jīng)控制和智能控制論. 哈爾濱: 哈爾濱工業(yè)大學(xué), 1998[2] 王志新. 智能模糊控制的若干問題研究. 北京: 知識(shí)產(chǎn)權(quán)出版, 2009[3] 張乃堯, 平凡. 神經(jīng)網(wǎng)絡(luò)與模糊控制. 北京: 清華大學(xué)出版社, 1998 [4] 王立新. 適應(yīng)模糊系統(tǒng)與控制設(shè)計(jì)與穩(wěn)定性分析. 機(jī)械工業(yè)出版社, 1995[5] 諸靜. 模糊控制原理與應(yīng)用. 北京: 機(jī)械工業(yè)出版社, 1995[6] 韓茂安, 顧圣士. 非線性系統(tǒng)的理論方法. 北京: 科學(xué)出版社, 2005[7] 洪奕光, 程代展. 非線性系統(tǒng)的分析與控制. 北京: 科學(xué)出版社,2005[8] 吳忠強(qiáng). 非線性系統(tǒng)的魯棒控制及應(yīng)用. 北京: 機(jī)械工業(yè)出版社, 2005[9] 王迎春, 楊珺. 復(fù)雜非線性系統(tǒng)模糊控制. 北京: 科學(xué)出版社， 2009[10] 劉小問. 非線性系統(tǒng)分析與控制引論. 北京: 清華大學(xué)出版社, 2008[11] 馮純伯. 非線性系統(tǒng)的魯棒控制. 北京: 科學(xué)技術(shù)出版社, 2004[12] 馮純伯, 樹岷. 非線性控制系統(tǒng)分析與設(shè)計(jì). 北京: 電子工業(yè)出版社, 1998 [13] 俞立. 魯棒控制——線性矩陣不等式處理方法. 北京: 清華大學(xué)出版社, 2002[14] 胡壽松. 自動(dòng)控制原理(第五版). 北京: 科學(xué)出版社, 2007[15] 李友善. 自動(dòng)控制原理(第三版). 北京: 國防工業(yè)出版社, 2005[16] 王宏華. 現(xiàn)代控制理論. 北京: 電子工業(yè)出版社, 2006[17] 劉豹, 唐萬生. 現(xiàn)代控制理論. 北京: 機(jī)械工業(yè)出版社, 2006[18] 佟紹成, 王濤, 王艷平, 唐澗濤. 模糊控制系統(tǒng)的發(fā)計(jì)及穩(wěn)定性分析. 北京: 科學(xué)出版社, 2004[19] 吳曉莉, 林哲輝. Matlab 輔助模糊系統(tǒng)設(shè)計(jì). 西安: 西安電子科技大學(xué)出版社, 2002[20] 商為炳. 非線性控制系統(tǒng)導(dǎo)論. 北京: 科學(xué)出版社, 1988[21] 韓志剛, 蔣愛萍, 王洪橋. 自適應(yīng)辨識(shí)預(yù)報(bào)和控制一多層遞階途徑. 哈爾濱: 黑龍江教育出版社, 1995[22] 韓志剛. 多層遞階方法及其應(yīng)用. 北京: 科學(xué)出版社,1989[23] 魏克新, 王云亮, 陳志敏, 高強(qiáng). MATLAB語言與自動(dòng)控制系統(tǒng)設(shè)計(jì). 北京: 機(jī)械工業(yè)出版社, 2004[24] 鄭大鐘. 線性系統(tǒng)理論. 北京: 清華大學(xué)出版社,2002[25] 高新波. 模糊聚類分析及其應(yīng)用. 西安: 西安電子科技大學(xué)出版社, 2003外文資料翻譯Static Output Feedback Control for Discretetime Fuzzy Bilinear SystemAbstract The paper addressed the problem of designing fuzzy static output feedback controller for TS discretetime fuzzy bilinear system (DFBS). Based on parallel distribute pensation method, some sufficient conditions are derived to guarantee the stability of the overall fuzzy system. The stabilization conditions are further formulated into linear matrix inequality (LMI) so that the desired controller can be easily obtained by using the Matlab LMI toolbox. In parison with the existing results, the drawbacks such as coordinate transformation, same output matrices have been eliminated. Finally, a simulation example shows that the approach is effective.Keywords discretetime fuzzy bilinear system (DFBS)。 static output feedback control。 fuzzy control。 linear matrix inequality (LMI)1 IntroductionIt is well known that TS fuzzy model is an effective tool for control of nonlinear systems where the nonlinear model is approximated by a set of linear local models connected by IFTHEN rules. Based on TS model, a great number of results have been obtained on concerning analysis and controller design[1][11]. Most of the above results are designed based on either state feedback control or observerbased control[1][7].Very few results deal with fuzzy output feedback[8][11]. The scheme of static output feedback control is very important and must be used when the system states are not pletely available for feedback. The static output feedback control for fuzzy systems with timedelay was addressed [9][10] and a robust H∞ controller via static output feedback was designed[11]. But the derived conditions are not solvable by the convex programming technique since they are bilinear matrix inequality problems. Moreover, it is noted that all of the aforementioned fuzzy systems were based on the TS fuzzy model with linear rule consequence.Bilinear systems exist between nonlinear and linear systems, which provide much better approximation of the original nonlinear systems than the linear systems [12].The research of bilinear systems has been paid a lot of attention and a series of results have been obtained[12][13].Considering the advantages of bilinear systems and fuzzy control, the fuzzy bilinear system (FBS) based on the TS fuzzy model with bilinear rule consequence was attracted the interest of researchers[14][16]. The paper [14] studied the robust stabilization for the FBS, then the result was extended to the FBS with timedelay[15]. The problem of robust stabilization for discretetime FBS (DFBS) was considered[16]. But all the above results are obtained via state feedback controller. In this paper, a new approach for designing a fuzzy static output feedback controller for the DFBS is proposed. Some sufficient conditions for synthesis of fuzzy static output feedback controller are derived in terms of linear matrix inequality (LMI) and the controller can be obtained by solving a set of LMIs. In parison with the existing literatures, the drawbacks such as coordinate transformation and same output matrices have been eliminated. Notation: In this paper, a real symmetric matrix denotes being a positive definite matrix. In symmetric block matrices, an asterisk (*) is used to represent a symmetric term and stands for a blockdiagonal matrix. The notionmeans.2 Problem formulationsConsider a DFBS that is represented by TS fuzzy bilinear model. The th rule of the DFBS is represented by the following form (1)Wheredenotes the fuzzy inference rule, is the number of fuzzy rules. is fuzzy set andis premise the state vector,is the control input and is the system output. The matrices are known matrices with appropriate dimensions. Since the static output feedback control is considered in this paper, we simply setand.By using singleton fuzzifier, product inference and centeraverage defuzzif