發(fā)布時間：2018-05-29 23:33

  本文選題：非線性系統(tǒng) + 自適應(yīng)神經(jīng)網(wǎng)絡(luò)控制�。� 參考：《渤海大學(xué)》2017年碩士論文

【摘要】：本文在國內(nèi)外不確定非線性系統(tǒng)相關(guān)研究基礎(chǔ)上,應(yīng)用神經(jīng)網(wǎng)絡(luò)控制理論,結(jié)合自適應(yīng)反步遞推設(shè)計和魯棒控制理論,研究了具有未建模動態(tài)非線性系統(tǒng)的控制問題,提出有效的自適應(yīng)神經(jīng)網(wǎng)絡(luò)控制方法.本文主要從下面兩個部分進(jìn)行論述:1.針對一類具有未建模動態(tài)的非仿射非線性時滯系統(tǒng),發(fā)展了一種自適應(yīng)神經(jīng)網(wǎng)絡(luò)智能控制方案。在控制器的設(shè)計過程中,應(yīng)用變量分離技術(shù)克服系統(tǒng)的全狀態(tài)時滯函數(shù)的設(shè)計困難,利用反步遞推設(shè)計方法和神經(jīng)網(wǎng)絡(luò)的萬能逼近能力提出了能夠保證閉環(huán)系統(tǒng)所有信號一致最終有界的自適應(yīng)神經(jīng)網(wǎng)絡(luò)控制方案。仿真結(jié)果驗證了所提出的控制方案的有效性。2.研究了一類具有未建模動態(tài)和輸入飽和的嚴(yán)格反饋非線性系統(tǒng)的自適應(yīng)神經(jīng)網(wǎng)絡(luò)控制問題。在控制設(shè)計過程中,應(yīng)用徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)近似逼近未知非線性函數(shù);反步遞推設(shè)計方法來構(gòu)造一種自適應(yīng)神經(jīng)網(wǎng)絡(luò)控制方案。所提出的控制方案保證了閉環(huán)系統(tǒng)的半全局有界性,同時通過估計神經(jīng)網(wǎng)絡(luò)權(quán)向量范數(shù)的最大值,使得控制系統(tǒng)只需要一個自適應(yīng)參數(shù),從而減小了計算量.仿真結(jié)果驗證了本章所提出的方法的有效性.
[Abstract]:In this paper, based on the research of uncertain nonlinear systems at home and abroad, the control problem of unmodeled dynamic nonlinear systems is studied by applying neural network control theory, combined with adaptive backstepping recursive design and robust control theory. An effective adaptive neural network control method is proposed. This paper mainly discusses the following two parts: 1. An adaptive neural network intelligent control scheme is developed for a class of non-affine nonlinear time-delay systems with unmodeled dynamics. In the design of the controller, the variable separation technique is used to overcome the difficulty of the design of the full state delay function of the system. Using the backstepping recursive design method and the universal approximation ability of the neural network, an adaptive neural network control scheme is proposed, which can guarantee the uniform and ultimately bounded signals of the closed-loop system. Simulation results verify the effectiveness of the proposed control scheme. The adaptive neural network control problem for a class of strictly feedback nonlinear systems with unmodeled dynamics and input saturation is studied. In the process of control design, the radial basis function neural network is used to approximate the unknown nonlinear function, and the backstepping recursive design method is used to construct an adaptive neural network control scheme. The proposed control scheme guarantees the semi-global boundedness of the closed-loop system, and by estimating the maximum value of the weight vector norm of the neural network, the control system only needs one adaptive parameter, thus reducing the computational complexity. The simulation results verify the effectiveness of the proposed method in this chapter.
【學(xué)位授予單位】：渤海大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP273;TP183

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本文編號：1952927