本文選題：滯環(huán) + 時(shí)滯�。� 參考：《東北電力大學(xué)》2017年碩士論文

【摘要】：隨著智能材料的不斷發(fā)現(xiàn)和愈加廣泛的應(yīng)用,對于嚴(yán)重影響系統(tǒng)控制精度的智能材料的固有特性—滯環(huán)現(xiàn)象越來越受到重視;與滯環(huán)現(xiàn)象類似,時(shí)滯現(xiàn)象也廣泛存在于各種裝置和實(shí)際系統(tǒng)中,它會降低系統(tǒng)控制的準(zhǔn)確性甚至使系統(tǒng)變得不穩(wěn)定;在實(shí)際系統(tǒng)中,通常輸出量是可測的,并且通常情況下具有明確的物理意義。因此,研究滯環(huán)非線性時(shí)滯系統(tǒng)的輸出反饋不但具有豐富的實(shí)際應(yīng)用背景,更具有理論價(jià)值。針對同時(shí)具有滯環(huán)、時(shí)滯等非線性因素的復(fù)雜非線性系統(tǒng),僅僅依靠線性系統(tǒng)理論或者傳統(tǒng)的PID等控制方法已經(jīng)不能很好的滿足控制目標(biāo),隨著對自適應(yīng)控制方法研究的深入,反推法在對含有未知參數(shù)的控制、改善過渡品質(zhì)等方法優(yōu)勢明顯,以反推法為基礎(chǔ)發(fā)展而來的動態(tài)面方法繼承反推法的優(yōu)勢,克服了反推法的缺點(diǎn),目前受到廣泛關(guān)注。本文將滯環(huán)非線性時(shí)滯系統(tǒng)作為主要研究對象,以動態(tài)面算法為主要控制器設(shè)計(jì)算法,取得了以下成果:(1)針對一類狀態(tài)變量全部可測的滯環(huán)非線性時(shí)滯系統(tǒng),提出了一種基于RBF神經(jīng)網(wǎng)絡(luò)的動態(tài)面控制方法。該方法的主要特點(diǎn)為:采用動態(tài)面的控制方法,大大簡化了控制器的設(shè)計(jì)過程;通過RBF神經(jīng)網(wǎng)絡(luò)估計(jì)未知時(shí)滯函數(shù),避免了建立一般用來處理時(shí)滯的復(fù)雜的Krasovskii函數(shù),放寬了對時(shí)滯函數(shù)的假設(shè),可以在時(shí)滯函數(shù)完全未知的情況下設(shè)計(jì)控制器,改進(jìn)了現(xiàn)有的動態(tài)面算法;穩(wěn)定性分析表明,控制方案可以保證由控制對象、控制律、調(diào)參律等組成的閉環(huán)系統(tǒng)穩(wěn)定并且通過參數(shù)的選取,可以使跟蹤誤差任意小;最后,通過仿真證明了方法的有效性。(2)針對一類只有輸出可測的滯環(huán)非線性時(shí)滯系統(tǒng),提出了一種基于RBF神經(jīng)網(wǎng)絡(luò)的動態(tài)面控制方法。該方法的主要特點(diǎn)為:在第三章采用動態(tài)面的控制方法以及通過RBF神經(jīng)網(wǎng)絡(luò)估計(jì)未知時(shí)滯函數(shù)的基礎(chǔ)上,針對狀態(tài)未知的情況,采用K-濾波器估計(jì)未知狀態(tài)變量,實(shí)現(xiàn)了在狀態(tài)變量未知情況下控制器的設(shè)計(jì);穩(wěn)定性分析表明,控制方案可以保證閉環(huán)系統(tǒng)的穩(wěn)定并且通過參數(shù)的選取,可以使跟蹤誤差任意小;最后,通過仿真證明了方法的有效性。
[Abstract]:With the continuous discovery of intelligent materials and their wider application, more and more attention has been paid to the inherent characteristics of intelligent materials, which seriously affect the control accuracy of the system, such as hysteresis, which is similar to the hysteresis phenomenon. The phenomenon of time-delay also exists widely in various devices and practical systems. It can reduce the accuracy of the control of the system and even make the system unstable. In the actual system, the output is usually measurable. And usually has a clear physical meaning. Therefore, the study of output feedback for hysteretic nonlinear time-delay systems not only has a rich background of practical application, but also has theoretical value. For complex nonlinear systems with nonlinear factors such as hysteresis, delay and so on, the theory of linear systems or the traditional control methods such as PID can not satisfy the control objectives well. The backstepping method has obvious advantages in controlling the unknown parameters and improving the quality of transition. The dynamic plane method developed on the basis of the backstepping method inherits the advantage of the backstepping method and overcomes the shortcomings of the backstepping method. At present, it has received extensive attention. In this paper, the hysteresis nonlinear time-delay system is taken as the main research object, and the dynamic plane algorithm is taken as the main controller design algorithm. The following results are obtained: 1) for a class of hysteretic nonlinear time-delay systems with all state variables measurable, A dynamic surface control method based on RBF neural network is proposed. The main features of this method are as follows: the controller design process is greatly simplified by using dynamic surface control method, and the unknown time-delay function is estimated by RBF neural network, which avoids the establishment of complex Krasovskii functions, which are generally used to deal with delays. The assumption of time-delay function is relaxed, the controller can be designed under the condition that the time-delay function is completely unknown, and the existing dynamic plane algorithm is improved, the stability analysis shows that the control scheme can be guaranteed by the control object, the control law and the control law. The closed-loop system composed of tuning parameter law and so on is stable and the tracking error can be reduced arbitrarily by selecting parameters. Finally, the simulation results show that the method is effective for a class of hysteretic nonlinear time-delay systems with only measurable output. A dynamic surface control method based on RBF neural network is proposed. The main features of this method are as follows: in Chapter 3, the unknown state variables are estimated by using K- filter based on the control method of dynamic plane and the estimation of unknown delay function by RBF neural network. The stability analysis shows that the stability of the closed-loop system can be guaranteed by the control scheme and the tracking error can be reduced arbitrarily by selecting the parameters. The effectiveness of the method is proved by simulation.
【學(xué)位授予單位】：東北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP13

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