【摘要】：在實(shí)際系統(tǒng)中,非線性、時滯和外部隨機(jī)干擾等因素是廣泛存在的,傳統(tǒng)的分析方法因?yàn)椴荒軠?zhǔn)確地對各種非線性不確定性因素進(jìn)行建模,容易造成控制性能的下降甚至失敗.因此,針對非線性系統(tǒng)領(lǐng)域的研究顯得尤為重要.本文針對幾類非完整和欠驅(qū)動系統(tǒng)中存在的非線性問題進(jìn)行了研究,設(shè)計了相應(yīng)的控制器并進(jìn)行了穩(wěn)定性分析,主要研究成果包括:1.對一類帶有未知參數(shù)的欠驅(qū)動柔性關(guān)節(jié)機(jī)器人系統(tǒng)進(jìn)行了研究,由于現(xiàn)存的研究成果均不同程度地具有過度參數(shù)化的特點(diǎn),增加了控制器設(shè)計的復(fù)雜度.本文找到引起上述問題的原因,研究它的自適應(yīng)跟蹤控制問題,主要解決了以下問題:(i)減少過度參數(shù)化帶來的復(fù)雜度,利用一個參數(shù)實(shí)現(xiàn)跟蹤控制.(ii)利用Backstepping控制方法找到一個合適的Lyapunov函數(shù),設(shè)計了控制器,并證明了所設(shè)計的控制器能夠使得閉環(huán)系統(tǒng)全局穩(wěn)定.2.對一類帶有不確定參數(shù)和時滯的非完整系統(tǒng),研究了它的狀態(tài)反饋鎮(zhèn)定問題.首次考慮了帶有多個時滯狀態(tài)的不確定非完整系統(tǒng)的自適應(yīng)控制問題,引入了時滯非線性漂移的邊界條件,利用基于動態(tài)增益的Lyapunov-Krasovskii方法和技巧,設(shè)計并構(gòu)造了相應(yīng)的狀態(tài)反饋?zhàn)赃m應(yīng)控制器,并證明了所設(shè)計的控制器能夠使得閉環(huán)系統(tǒng)全局漸近穩(wěn)定.3.對一類帶有未知系數(shù)和外界干擾的鏈?zhǔn)椒蔷�性系統(tǒng),研究了它的固定時間跟蹤問題.利用增加冪次積分法將有限時間跟蹤問題變換為固定時間跟蹤問題,所設(shè)計的控制律能夠保證系統(tǒng)狀態(tài)在固定時間跟蹤到期望軌跡.此類帶有快速響應(yīng)和短時切換的固定時間鎮(zhèn)定問題在實(shí)際應(yīng)用中具有非常高的理論意義和應(yīng)用價值.4.對一類典型的欠驅(qū)動非完整橋式吊車系統(tǒng)進(jìn)行了研究,此類系統(tǒng)具有高度的耦合性和非線性,分析了基于理論模型的仿真結(jié)果同實(shí)際運(yùn)行結(jié)果存在差異的主要原因,通過引入RBF神經(jīng)網(wǎng)絡(luò)自適應(yīng)控制算法對非線性不確定因素進(jìn)行逼近,并結(jié)合確定學(xué)習(xí)方法設(shè)計了基于RBF的神經(jīng)網(wǎng)絡(luò)自適應(yīng)控制器,實(shí)現(xiàn)了針對實(shí)際吊車系統(tǒng)RE3DCrane的控制和應(yīng)用,所設(shè)計的控制器魯棒性較好且有一定的實(shí)用價值.
[Abstract]:In the practical system, the factors such as nonlinearity, time-delay and external random disturbance exist widely. Because the traditional analysis method can not accurately model all kinds of nonlinear uncertain factors, it is easy to cause the degradation or even failure of control performance. Therefore, the study of nonlinear systems is particularly important. In this paper, the nonlinear problems in some nonholonomic and underactuated systems are studied, the corresponding controllers are designed and the stability is analyzed. The main research results include: 1. A class of underactuated flexible joint robot systems with unknown parameters is studied. Due to the overparameterization of existing research results, the complexity of controller design is increased. In this paper, the causes of the above problems are found, and its adaptive tracking control problem is studied. The main solutions are as follows: (i) reduces the complexity caused by excessive parameterization. The tracking control. (ii) is realized by one parameter. The Backstepping control method is used to find a suitable Lyapunov function. The controller is designed, and it is proved that the designed controller can make the closed-loop system globally stable. 2. The state feedback stabilization problem for a class of nonholonomic systems with uncertain parameters and delays is studied. The adaptive control problem of uncertain nonholonomic systems with multiple time-delay states is considered for the first time. The boundary condition of time-delay nonlinear drift is introduced, and the Lyapunov-Krasovskii method and technique based on dynamic gain are used. The corresponding state feedback adaptive controller is designed and constructed, and it is proved that the designed controller can make the closed-loop system asymptotically stable globally. The fixed time tracking problem for a class of chain nonlinear systems with unknown coefficients and external disturbances is studied. The finite time tracking problem is transformed into a fixed time tracking problem by adding power integration method. The designed control law can ensure that the state of the system can be tracked to the desired trajectory at a fixed time. This kind of fixed time stabilization problem with fast response and short time switching is of great theoretical significance and practical value in practical applications. A kind of typical underactuated non-holonomic bridge crane system is studied in this paper. This kind of system is highly coupled and nonlinear. The main reasons for the difference between the simulation results based on the theoretical model and the actual operation results are analyzed. By introducing RBF neural network adaptive control algorithm to approximate nonlinear uncertain factors and combining with deterministic learning method, a neural network adaptive controller based on RBF is designed. The control and application of RE3DCrane for actual crane system are realized. The controller designed has good robustness and practical value.
【學(xué)位授予單位】：曲阜師范大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：O231

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