【摘要】：Euler-Lagrange系統(tǒng)是一種具有代表性的非線性系統(tǒng),它可以描述許多復雜的動力學問題,因此針對Euler-Lagrange系統(tǒng)軌跡跟蹤控制的研究具有很好的實際應用意義和理論研究價值。本文提出了三種模糊神經(jīng)自適應控制方法,為Euler-Lagrange系統(tǒng)的軌跡跟蹤控制問題提供了有效的解決辦法。首先,針對Euler-Lagrange系統(tǒng)中存在模型不確定性和未知外界擾動等問題,本文提出了一種基于變論域模糊系統(tǒng)的魯棒自適應Backstepping跟蹤控制方法。變論域模糊系統(tǒng)是由帶有變伸縮因子的模糊基函數(shù)構(gòu)成,通過伸縮因子根據(jù)系統(tǒng)狀態(tài)的自適應在線調(diào)整,實現(xiàn)了模糊系統(tǒng)輸入空間的自適應和模糊基函數(shù)的自適應,在不增加模糊規(guī)則的前提下提高控制精度。仿真研究驗證了所提出方法的有效性。其次,為減少逼近器輸入維度,降低運算復雜度,本文提出了一種基于極速學習神經(jīng)網(wǎng)絡(luò)的混合前饋-反饋魯棒自適應跟蹤控制方法。通過設(shè)計前饋極速學習神經(jīng)網(wǎng)絡(luò)逼近器,實現(xiàn)了對系統(tǒng)不確定性的有效逼近;與傳統(tǒng)反饋逼近控制相比,所提出的前饋逼近器只需要參考量作為神經(jīng)網(wǎng)絡(luò)的輸入,不僅減少了逼近器的輸入維度,而且減少了隱含層節(jié)點數(shù),從而極大精簡了逼近器結(jié)構(gòu),降低了運算復雜度;此外,設(shè)計H∞魯棒補償項,消除未知外界擾動和逼近誤差對控制精度的影響。仿真研究驗證了所提出方法的有效性。最后,針對速度不可測的Euler-Lagrange系統(tǒng),提出了一種基于自組織模糊神經(jīng)觀測器的H∞輸出反饋控制方法。通過設(shè)計自組織模糊神經(jīng)速度觀測器,實現(xiàn)對未知速度的準確估計,并且該觀測器能夠自動在線生成模糊規(guī)則和修剪冗余規(guī)則,極大降低了運算復雜度;設(shè)計位置跟蹤誤差和速度誤差相結(jié)合的滑模面,將系統(tǒng)不確定性和未知外界擾動重組為集總非線性,并設(shè)計自組織模糊神經(jīng)網(wǎng)絡(luò)逼近器對其進行在線自適應逼近;進而設(shè)計H∞魯棒補償項,進一步消除逼近誤差,以提高控制精度和系統(tǒng)魯棒性。仿真結(jié)果驗證了上述方法的有效性。
[Abstract]:Euler-Lagrange system is a representative nonlinear system, which can describe many complex dynamic problems. Therefore, the research on trajectory tracking control of Euler-Lagrange system has a good practical significance and theoretical research value. In this paper, three kinds of fuzzy neural adaptive control methods are proposed, which provide an effective solution to the trajectory tracking control problem of Euler-Lagrange system. Firstly, a robust adaptive Backstepping tracking control method based on variable universe fuzzy systems is proposed to solve the problems of model uncertainty and unknown external disturbances in Euler-Lagrange systems. The variable domain fuzzy system is composed of fuzzy basis function with variable expansion factor. By adjusting the expansion factor according to the adaptive on-line state of the system, the adaptive input space of fuzzy system and the adaptation of fuzzy basis function are realized. The control accuracy is improved without adding fuzzy rules. Simulation results show that the proposed method is effective. Secondly, in order to reduce the input dimension of the approximator and reduce the computational complexity, a hybrid feedforward and feedback robust adaptive tracking control method based on extreme learning neural network is proposed in this paper. The feedforward learning neural network approximator is designed to achieve the effective approximation of the system uncertainty. Compared with the traditional feedback approximation control, the proposed feedforward approximator only needs reference as the input of the neural network. It not only reduces the input dimension of the approximator, but also reduces the number of hidden layer nodes, which greatly simplifies the structure of the approximator and reduces the computational complexity. In addition, the H 鈭，

本文編號：2162768