發(fā)布時間：2018-10-25 17:53

【摘要】：實際工程系統(tǒng)往往表現(xiàn)為非線性系統(tǒng),而且系統(tǒng)中不可避免地存在各種不確定性和外部隨機擾動。這些不確定性和隨機擾動會對系統(tǒng)的穩(wěn)定性和性能產(chǎn)生影響。因此,隨機非線性系統(tǒng)的控制問題研究具有重要的理論意義。在工程實踐中,如何克服不確定性和隨機擾動對飛行控制系統(tǒng)穩(wěn)定性的影響進而保證飛行安全,是具有實際應用價值的課題。因此,本論文主要針對隨機非線性不確定系統(tǒng)進行鎮(zhèn)定控制器的分析和設計,并將理論研究成果應用于飛行系統(tǒng)的穩(wěn)定性控制中。主要研究內(nèi)容如下:首先對國內(nèi)外隨機非線性系統(tǒng)控制問題研究的發(fā)展現(xiàn)狀進行介紹總結,分析目前研究熱點與需解決的問題;在隨機Lyapunov穩(wěn)定性理論框架下,將Backstepping技術與模糊神經(jīng)網(wǎng)絡方法相結合,通過構建四層模糊神經(jīng)網(wǎng)絡,使得輸出權值可自適應調整,從而設計出使得一類純反饋隨機非線性不確定系統(tǒng)的狀態(tài)依概率有界的自適應控制器,設計方法有效地減少了可調參數(shù)的數(shù)目;針對Backstepping的不足,為改進控制器設計,引入動態(tài)面控制方法,通過一階低通濾波器的應用避免參數(shù)膨脹,并簡化了實際控制器結構,降低計算量,設計出使得閉環(huán)系統(tǒng)半全局一致最終有界的自適應控制器;此外,通過減小濾波時間來加快系統(tǒng)狀態(tài)收斂且減小濾波誤差;將所獲得的理論成果應用于含隨機擾動的高超速飛行器縱向模型控制中,設計出自適應模糊神經(jīng)網(wǎng)絡動態(tài)面控制器,以保證閉環(huán)系統(tǒng)信號是半全局一致最終有界的,仿真結果驗證了該方法的有效性;最后,對全文進行總結與展望。
[Abstract]:Practical engineering systems are often nonlinear systems, and there are inevitably various uncertainties and external random disturbances in the systems. These uncertainties and random disturbances will affect the stability and performance of the system. Therefore, it is of great theoretical significance to study the control problem of stochastic nonlinear systems. In engineering practice, how to overcome the influence of uncertainty and random disturbance on the stability of flight control system and ensure flight safety is a subject of practical application value. Therefore, the stabilization controller of stochastic nonlinear uncertain systems is analyzed and designed in this paper, and the theoretical research results are applied to the stability control of flight systems. The main research contents are as follows: firstly, the development of stochastic nonlinear system control problems at home and abroad is introduced and summarized, and the current research hotspots and problems to be solved are analyzed. Combining Backstepping technology with fuzzy neural network method, the output weight can be adjusted adaptively by constructing a four-layer fuzzy neural network. Thus, an adaptive controller is designed, which makes the state of a class of pure feedback stochastic nonlinear uncertain systems bounded by probability. The design method effectively reduces the number of adjustable parameters. The dynamic surface control method is introduced to avoid the parameter expansion through the application of the first-order low-pass filter. The structure of the controller is simplified, the computation is reduced, and an adaptive controller is designed to make the closed-loop system semi-global uniform and ultimately bounded. The filtering time is reduced to accelerate the state convergence of the system and the filtering error is reduced. The obtained theoretical results are applied to the longitudinal model control of high speed vehicles with random disturbances, and an adaptive fuzzy neural network dynamic surface controller is designed. In order to ensure that the closed-loop system signal is semi-global uniform and ultimately bounded, the simulation results verify the effectiveness of the method. Finally, the paper is summarized and prospected.
【學位授予單位】：南京航空航天大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP273

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