本文選題：不確定非線性系統(tǒng) + 后推方法��； 參考：《大連海事大學(xué)》2016年博士論文

【摘要】：近年來,基于在線逼近的不確定下三角非線性系統(tǒng)自適應(yīng)控制吸引了廣大學(xué)者的研究興趣。本文對(duì)不確定下三角型非線性系統(tǒng)的自適應(yīng)神經(jīng)網(wǎng)絡(luò)控制設(shè)計(jì)及其在自主式水下機(jī)器人的三維軌跡跟蹤控制應(yīng)用方面進(jìn)行了研究,主要研究工作如下：1.針對(duì)一類增益已知的不確定下三角型非線性系統(tǒng),提出一種基于指令濾波技術(shù)的自適應(yīng)神經(jīng)網(wǎng)絡(luò)控制方法。該方法通過一個(gè)二階濾波器而非直接解析地對(duì)虛擬控制律求導(dǎo),從而顯著地簡(jiǎn)化了后推控制器的設(shè)計(jì)過程,避免了“計(jì)算爆炸”問題。此外,將神經(jīng)網(wǎng)絡(luò)理想權(quán)值的范數(shù)做為在線估計(jì)參數(shù),使在線學(xué)習(xí)參數(shù)的個(gè)數(shù)顯著地減少,降低了控制器的計(jì)算負(fù)擔(dān)。這兩種控制技術(shù)的有機(jī)結(jié)合,使得算法結(jié)構(gòu)簡(jiǎn)單、計(jì)算量小而易于工程實(shí)現(xiàn)。對(duì)系統(tǒng)進(jìn)行了基于李雅普諾夫穩(wěn)定性理論的穩(wěn)定性分析,表明系統(tǒng)中的所有信號(hào)是半全局一致最終有界的,且可以通過調(diào)節(jié)設(shè)計(jì)參數(shù)使得跟蹤誤差盡可能的小。2.針對(duì)一類增益未知的不確定下三角型非線性系統(tǒng),進(jìn)行自適應(yīng)神經(jīng)網(wǎng)絡(luò)控制器設(shè)計(jì)。首先采用動(dòng)態(tài)面控制技術(shù)以解決傳統(tǒng)后推方法中存在的“計(jì)算爆炸”問題,然后將控制器設(shè)計(jì)過程中出現(xiàn)的未知部分保留到下一步,依此類推,直到控制設(shè)計(jì)的最后一步,只使用一個(gè)神經(jīng)網(wǎng)絡(luò)逼近器實(shí)現(xiàn)實(shí)際控制律中的未知部分,最后將神經(jīng)網(wǎng)絡(luò)權(quán)值的范數(shù)做為在線估計(jì)參數(shù),使在線學(xué)習(xí)參數(shù)的個(gè)數(shù)顯著地減少。該方法所設(shè)計(jì)的控制器結(jié)構(gòu)簡(jiǎn)單,使計(jì)算負(fù)擔(dān)大為減少。應(yīng)用李雅普諾夫穩(wěn)定性理論對(duì)閉環(huán)系統(tǒng)進(jìn)行分析,得出系統(tǒng)中所有信號(hào)半全局一致最終有界的結(jié)論,且可以通過調(diào)節(jié)設(shè)計(jì)參數(shù)使得跟蹤誤差盡可能的小。3.針對(duì)模型未知的自主式水下機(jī)器人非線性動(dòng)力學(xué)模型,考慮其存在不確定部分和受到外界干擾的作用,提出兩種自適應(yīng)神經(jīng)網(wǎng)絡(luò)控制器設(shè)計(jì)方法。分別采用指令濾波技術(shù)和動(dòng)態(tài)面控制技術(shù)來避免傳統(tǒng)后推方法中存在的“計(jì)算爆炸”問題,然后結(jié)合“最少學(xué)習(xí)參數(shù)”的設(shè)計(jì)思想,使在線學(xué)習(xí)參數(shù)的個(gè)數(shù)大為減少。所提出的控制算法具有結(jié)構(gòu)簡(jiǎn)單、計(jì)算量小、易于工程實(shí)現(xiàn)等特點(diǎn)。對(duì)系統(tǒng)進(jìn)行了基于李雅普諾夫穩(wěn)定性理論的穩(wěn)定性分析,表明系統(tǒng)中的所有信號(hào)是半全局一致最終有界的,且可以通過調(diào)節(jié)設(shè)計(jì)參數(shù)使得跟蹤誤差盡可能的小。最后,利用MATLAB進(jìn)行了數(shù)值仿真研究,仿真結(jié)果表明所設(shè)計(jì)的控制器可以實(shí)現(xiàn)自主式水下機(jī)器人的三維軌跡精確跟蹤控制。
[Abstract]:In recent years, adaptive control of uncertain lower triangular nonlinear systems based on online approximation has attracted many scholars' interest. In this paper, the design of adaptive neural network control for uncertain lower triangular nonlinear system and its application in 3D trajectory tracking control of autonomous underwater vehicle are studied. The main research work is as follows: 1. An adaptive neural network control method based on instruction filtering is proposed for a class of uncertain lower triangular nonlinear systems with known gain. The method uses a second-order filter instead of a direct analytical derivation to the virtual control law, which simplifies the design process of the backstepping controller and avoids the problem of "computational explosion". In addition, the norm of the ideal weight of neural network is taken as the on-line estimation parameter, so that the number of on-line learning parameters is significantly reduced and the computational burden of the controller is reduced. The combination of these two control techniques makes the algorithm simple and easy to implement. The stability analysis of the system based on Lyapunov stability theory shows that all the signals in the system are semi-global uniform and ultimately bounded, and the tracking error can be as small as possible by adjusting the design parameters. An adaptive neural network controller is designed for a class of uncertain lower triangular nonlinear systems with unknown gain. Firstly, the dynamic surface control technique is used to solve the problem of "computational explosion" in the traditional backstepping method, and then the unknown parts in the controller design are retained to the next step, and so on, until the last step of the control design. Only one neural network approximator is used to realize the unknown part of the actual control law. Finally, the norm of neural network weights is taken as the on-line estimation parameter, so that the number of on-line learning parameters is significantly reduced. The controller designed by this method is simple in structure and greatly reduces the computational burden. By using Lyapunov stability theory, the closed-loop system is analyzed, and the conclusion that all the signals in the system are semi-global uniform and finally bounded is obtained, and the tracking error can be as small as possible by adjusting the design parameters. Aiming at the nonlinear dynamic model of autonomous underwater vehicle (AUV) with unknown model, two adaptive neural network controller design methods are proposed, considering the uncertain part of the model and the effect of external disturbance. Instruction filter technique and dynamic surface control technique are adopted to avoid the problem of "computational explosion" in traditional backstepping method, and then the number of online learning parameters is greatly reduced by combining the design idea of "minimum learning parameter". The proposed control algorithm has the advantages of simple structure, small computation and easy engineering implementation. The stability analysis of the system based on Lyapunov stability theory shows that all the signals in the system are semi-global uniform and ultimately bounded, and the tracking error can be minimized by adjusting the design parameters. Finally, the numerical simulation is carried out with MATLAB, and the simulation results show that the controller can realize the accurate tracking control of the autonomous underwater vehicle.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP273.2

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