【摘要】：在航空航天領(lǐng)域,板殼結(jié)構(gòu)有著廣泛的應(yīng)用背景,其形狀控制和振動(dòng)控制一直是系統(tǒng)設(shè)計(jì)和工程應(yīng)用中的重點(diǎn)和難點(diǎn)。當(dāng)前,利用智能材料對(duì)板殼結(jié)構(gòu)實(shí)施有效的主動(dòng)激勵(lì)來(lái)抑制振動(dòng)的方法是一種有效的方法。但傳統(tǒng)的由電、磁信號(hào)激發(fā)的智能材料,需要附加復(fù)雜的電磁激發(fā)裝置,不利于系統(tǒng)的輕質(zhì)小型化,同時(shí)其與激發(fā)裝置之間需要導(dǎo)線連接,易引起電磁噪音干擾,而影響控制信號(hào)的傳送準(zhǔn)確性和實(shí)時(shí)性。而新型的鑭改性鋯鈦酸鉛(PLZT)陶瓷,可直接將光能轉(zhuǎn)化為機(jī)械能,不受電磁干擾的影響,適于在太空環(huán)境下實(shí)施非接觸激勵(lì)及遠(yuǎn)程控制,有著廣闊的應(yīng)用前景。本文以層合光致伸縮PLZT驅(qū)動(dòng)器的板和殼結(jié)構(gòu)為對(duì)象,研究其智能振動(dòng)主動(dòng)控制技術(shù),對(duì)與其相關(guān)的驅(qū)動(dòng)器構(gòu)型、動(dòng)力學(xué)建模、驅(qū)動(dòng)器位置優(yōu)化以及單模態(tài)和多模態(tài)智能主動(dòng)控制方法等幾個(gè)方面的問(wèn)題進(jìn)行了相應(yīng)的理論研究。論文的主要工作和創(chuàng)新性成果如下:(1)基于PLZT驅(qū)動(dòng)器的光-熱-力-電多場(chǎng)耦合本構(gòu)模型,采用數(shù)值仿真方法分析了影響PLZT驅(qū)動(dòng)器性能的主要因素;對(duì)當(dāng)前常用的驅(qū)動(dòng)器構(gòu)型進(jìn)行了分析比較,分析指出現(xiàn)有的驅(qū)動(dòng)器構(gòu)型在外部光源作用下“只能伸長(zhǎng)不能縮短”,因而只能產(chǎn)生單向膜控制力。進(jìn)一步,提出了兩種能夠產(chǎn)生正負(fù)膜控制力的多片組合驅(qū)動(dòng)器構(gòu)型,成功地克服了現(xiàn)有驅(qū)動(dòng)器構(gòu)型的缺陷,這種構(gòu)型在曲殼結(jié)構(gòu)的主動(dòng)控制中具有明顯的優(yōu)勢(shì),能夠顯著的提高驅(qū)動(dòng)器的作動(dòng)效率;(2)基于板殼結(jié)構(gòu)振動(dòng)理論,建立了可適用于不同結(jié)構(gòu)類型、不同幾何參數(shù)的光電層合板殼結(jié)構(gòu)的通用動(dòng)力學(xué)模型,利用此模型可以進(jìn)一步推導(dǎo)出層合有光致驅(qū)動(dòng)器的不同類型板殼結(jié)構(gòu),如矩形板、圓柱殼、球殼,錐殼等結(jié)構(gòu)的系統(tǒng)動(dòng)力學(xué)方程;基于所建立的動(dòng)力學(xué)模型,利用模態(tài)展開(kāi)技術(shù)建立了光電層合板殼結(jié)構(gòu)的模態(tài)控制方程。(3)結(jié)合PLZT驅(qū)動(dòng)器切換致動(dòng)和非線性驅(qū)動(dòng)的特性,提出了獨(dú)立模態(tài)變結(jié)構(gòu)模糊控制器,與現(xiàn)有的常規(guī)李亞普洛夫控制(常光強(qiáng)控制)和速度反饋控制(變光強(qiáng)控制)相比,該控制器具有兩方面的優(yōu)勢(shì):一方面對(duì)光照方向的切換函數(shù)進(jìn)行了優(yōu)化設(shè)計(jì),得到了最優(yōu)光照方向切換函數(shù);另一方面對(duì)光強(qiáng)的控制采用量化因子自調(diào)節(jié)模糊控制器,充分考慮了驅(qū)動(dòng)器的驅(qū)動(dòng)特性。所提出的控制器綜合了模糊控制與變結(jié)構(gòu)控制的優(yōu)點(diǎn),是一種不依賴于系統(tǒng)精確模型的智能控制器,能夠克服驅(qū)動(dòng)器的非線性驅(qū)動(dòng)特性,其控制效果明顯優(yōu)于速度反饋控制。(4)結(jié)合算例給出了相應(yīng)受控模態(tài)在驅(qū)動(dòng)器位置變化時(shí)其模態(tài)控制因子的變化規(guī)律;分析得出了:對(duì)于確定的模態(tài),存在一個(gè)或多個(gè)極值區(qū)域;在該區(qū)域,驅(qū)動(dòng)器產(chǎn)生的模態(tài)控制因子幅值明顯大于其他貼片區(qū)域;而且,隨著模態(tài)半波數(shù)的變化,極值區(qū)域的分布會(huì)發(fā)生變化。進(jìn)一步,為了實(shí)現(xiàn)對(duì)多個(gè)受控模態(tài)的振動(dòng)同時(shí)進(jìn)行抑制,需要將驅(qū)動(dòng)器粘貼在能夠?qū)λ惺芸啬B(tài)都產(chǎn)生盡可能大的模態(tài)因子的位置,為此,提出了以受控模態(tài)控制力因子的絕對(duì)值之和為優(yōu)化函數(shù)及以驅(qū)動(dòng)器的位置坐標(biāo)為優(yōu)化變量的板殼結(jié)構(gòu)的多模態(tài)振動(dòng)驅(qū)動(dòng)器位置遺傳優(yōu)化算法,并結(jié)合本文提出的多片組合驅(qū)動(dòng)器構(gòu)型對(duì)板殼結(jié)構(gòu)的驅(qū)動(dòng)器位置進(jìn)行了優(yōu)化設(shè)計(jì),計(jì)算得到了板殼結(jié)構(gòu)在相應(yīng)驅(qū)動(dòng)器構(gòu)型下的驅(qū)動(dòng)器優(yōu)化布片位置。(5)針對(duì)光電層合板殼結(jié)構(gòu)的多模態(tài)主動(dòng)控制問(wèn)題,提出了最優(yōu)模糊主動(dòng)控制算法,該算法是由當(dāng)前成熟的LQR控制與模糊控制組合而成,在算法設(shè)計(jì)過(guò)程中將結(jié)構(gòu)系統(tǒng)控制和驅(qū)動(dòng)器控制分開(kāi)考慮,設(shè)計(jì)步驟分為兩步:首先基于簡(jiǎn)化的線性系統(tǒng)模型設(shè)計(jì)LQR控制律,然后通過(guò)模糊控制器調(diào)節(jié)光電驅(qū)動(dòng)器的輸入光強(qiáng)使其輸出的光致應(yīng)變逼近最優(yōu)控制量。該方法解決了當(dāng)前光電層合系統(tǒng)不能直接應(yīng)用線性系統(tǒng)控制方法的矛盾,通過(guò)將一個(gè)復(fù)雜的問(wèn)題進(jìn)行分解簡(jiǎn)化了控制器的設(shè)計(jì),實(shí)現(xiàn)了光電層合板殼結(jié)構(gòu)的多模態(tài)主動(dòng)控制。結(jié)合該算法,通過(guò)仿真對(duì)比,對(duì)本文提出的多模態(tài)驅(qū)動(dòng)器位置優(yōu)化準(zhǔn)則函數(shù)的合理性進(jìn)行了驗(yàn)證。(6)將驅(qū)動(dòng)器和結(jié)構(gòu)系統(tǒng)作為整體考慮,提出了模糊神經(jīng)網(wǎng)絡(luò)控制(FNNC)和自組織模糊滑�？刂�(SOFSMC)等兩種多模態(tài)主動(dòng)控制算法。提出的FNNC主動(dòng)控制算法綜合了模糊控制和神經(jīng)網(wǎng)絡(luò)控制的優(yōu)點(diǎn),為了簡(jiǎn)化系統(tǒng),所提出的模糊神經(jīng)網(wǎng)絡(luò)基于RBF網(wǎng)絡(luò),并采用兩輸入單輸出結(jié)構(gòu)。然而,在多模態(tài)振動(dòng)問(wèn)題中,控制變量的個(gè)數(shù)要多于控制器的輸入個(gè)數(shù),為了解決這一問(wèn)題,參考欠驅(qū)動(dòng)控制理論中所采用的二級(jí)滑模面思想,首先以各受控模態(tài)的位移和它們的速度信號(hào)線性組合構(gòu)成各模態(tài)的一級(jí)滑模函數(shù),然后將所有一級(jí)滑模函數(shù)進(jìn)行線性組合構(gòu)成二級(jí)滑模函數(shù);最后將二級(jí)滑模函數(shù)和它的導(dǎo)數(shù)作為FNNC的輸入變量。所提出的FNNC主動(dòng)控制器不依賴于系統(tǒng)的數(shù)學(xué)模型,具有模糊規(guī)則和隸屬度函數(shù)在線學(xué)習(xí)能力。(7)提出的SOFSMC主動(dòng)振動(dòng)控制算法通過(guò)引入二級(jí)滑模函數(shù),降低了系統(tǒng)的控制階數(shù),簡(jiǎn)化了模糊控制系統(tǒng)的結(jié)構(gòu);通過(guò)引入自組織學(xué)習(xí)算法實(shí)現(xiàn)了控制器規(guī)則的在線學(xué)習(xí),克服了常規(guī)模糊滑模控制器依賴系統(tǒng)規(guī)則的缺點(diǎn);系統(tǒng)控制中模糊滑模的使用柔化了控制信號(hào),避免了一般滑�？刂频亩墩瘳F(xiàn)象;采用了單值模糊規(guī)則參數(shù),這種單值模糊規(guī)則參數(shù)可以通過(guò)自組織學(xué)習(xí)算法進(jìn)行自動(dòng)調(diào)節(jié);所使用的自組織學(xué)習(xí)算法與當(dāng)前公開(kāi)報(bào)道的文獻(xiàn)是不相同的,其是依據(jù)光電層合結(jié)構(gòu)多模態(tài)振動(dòng)系統(tǒng)線性自回歸平滑模型推導(dǎo)得到的新的自組織學(xué)習(xí)算法。為了驗(yàn)證所提出的智能主動(dòng)控制算法的有效性,結(jié)合板殼結(jié)構(gòu)的多模態(tài)主動(dòng)控制算例進(jìn)行了仿真。
[Abstract]:In the field of aeronautics and astronautics, the shell structure has a wide application background, and its shape control and vibration control have been the key and difficult point in the system design and engineering application. At present, the method for suppressing the vibration by using the intelligent material to implement effective active excitation to the plate shell structure is an effective method. But the traditional intelligent material excited by the electric and magnetic signals needs to be added with a complex electromagnetic excitation device, which is not beneficial to the light miniaturization of the system, and meanwhile, a wire connection is required between the intelligent material and the excitation device, so that the electromagnetic noise interference can be easily caused, and the transmission accuracy and the real-time property of the control signal are influenced. The new type of modified lead titanate (PLZT) ceramic can directly convert light energy into mechanical energy, and is not affected by electromagnetic interference. It is suitable for non-contact excitation and remote control in space environment, and has wide application prospect. In this paper, based on the plate and shell structure of the layer-based telescopic PLZT driver, the intelligent vibration active control technology is studied, and its related drive configuration and dynamic modeling are studied. The problems of drive position optimization, single mode and multi-mode intelligent active control method are studied in this paper. The main work and innovative achievements of the paper are as follows: (1) Based on the light-thermal-force-electric multi-field coupling constitutive model of the PLZT driver, the main factors that influence the performance of the PLZT driver are analyzed by the numerical simulation method; and compared with the current common driver configuration, The analysis indicates that the current driver configuration is "can only be stretched and cannot be shortened" under the action of an external light source, and thus the one-way film control force can only be generated. Furthermore, two kinds of combined drive configurations which can produce positive and negative film control force are put forward, and the defects of the existing drive configuration are successfully overcome, and the configuration has obvious advantages in the active control of the curved shell structure, and can obviously improve the operation efficiency of the driver; (2) Based on the vibration theory of the plate-shell structure, a general-purpose dynamic model of the shell structure of the photovoltaic laminated plate which can be applied to the types of different structures and different geometric parameters is established, and different types of plate-shell structures such as rectangular plates, The system dynamics equation of the structure of the cylindrical shell, the spherical shell, the cone shell and the like is solved, and the mode control equation of the shell structure of the photoelectric laminated plate is established by means of the mode expansion technology based on the established kinetic model. (3) In combination with the characteristics of the PLZT driver switching and non-linear driving, the independent mode variable structure fuzzy controller is proposed. Compared with the conventional conventional Lyapunov control (constant light intensity control) and speed feedback control (variable light intensity control), the controller has two advantages: On the one hand, the optimal design of the switching function of the light direction is optimized, and the optimal illumination direction switching function is obtained; on the other hand, the self-adjusting fuzzy controller of the quantization factor is adopted for controlling the light intensity, and the driving characteristics of the driver are fully taken into account. The proposed controller has the advantages of fuzzy control and variable structure control. It is a kind of intelligent controller which does not depend on the precise model of the system, can overcome the nonlinear drive characteristic of the driver, and the control effect is obviously superior to the speed feedback control. (4) The variation law of the mode control factors of the corresponding controlled modes in the drive position is given in the paper. The results are as follows: for the determined mode, there are one or more extreme regions; in this region, And the amplitude of the mode control factor generated by the driver is obviously larger than that of the other patch areas, and the distribution of the extreme region can change as the mode half-wave number is changed. Further, in order to achieve the simultaneous suppression of the vibrations of the plurality of controlled modalities, it is necessary to attach the drive to a position capable of generating as large a modal factor as possible for all of the controlled modalities, for this purpose, The invention provides a multi-modal vibration driver position genetic optimization algorithm based on the sum of the absolute value of the controlled mode control force factor as an optimization function and the plate shell structure with the position coordinate of the driver as an optimization variable, And the drive position of the plate shell structure is optimized and designed in combination with the multi-piece combination driver configuration set forth herein, and the position of the driver in the corresponding driver configuration of the plate shell structure is calculated. (5) The optimal fuzzy active control algorithm is proposed for the multi-mode active control of the shell structure of the laminated plate. The algorithm is composed of the current mature LQR control and the fuzzy control, and the control of the structure and the control of the driver are taken into account during the design of the algorithm. The design step is divided into two steps: firstly, designing the LQR control law based on the simplified linear system model, and then adjusting the input light intensity of the photoelectric driver by the fuzzy controller to approximate the optimal control amount of the light-induced strain output by the photoelectric driver. The method solves the contradiction that the current photoelectric lamination system cannot directly apply the control method of the linear system, simplifies the design of the controller by decomposing a complex problem, and realizes the multi-mode active control of the shell structure of the photoelectric laminated plate. In this paper, the rationality of the multi-modal drive position optimization criterion function proposed in this paper is verified by simulation and comparison. (6) As a whole, two kinds of multi-mode active control algorithms such as fuzzy neural network control (FNNC) and self-organizing fuzzy sliding mode control (SOFSMC) are put forward. The proposed FNNC active control algorithm has the advantages of fuzzy control and neural network control. In order to simplify the system, the proposed fuzzy neural network is based on the RBF network and adopts two input single-output structures. However, in the multi-modal vibration problem, the number of control variables is more than the number of input of the controller, First, the first-order sliding mode function of each mode is formed by linear combination of the displacement of each controlled mode and their velocity signals, and then all the first-level sliding mode functions are linearly combined to form a two-level sliding mode function; and finally, the second-order sliding mode function and the derivative thereof are used as input variables of the FNNC. The proposed FNNC active controller does not rely on the mathematical model of the system, and has the online learning ability of the fuzzy rule and the membership function. (7) The proposed SOFSMC active vibration control algorithm reduces the control order of the system by introducing the two-stage sliding mode function, simplifies the structure of the fuzzy control system, and realizes on-line learning of the controller rule by introducing the self-organizing learning algorithm, The defect that the conventional fuzzy sliding mode controller relies on the system rule is overcome, the use of the fuzzy sliding mode in the system control is flexible to control the signal, the chattering phenomenon of the general sliding mode control is avoided, and the single-value fuzzy rule parameter is adopted, this single-valued fuzzy rule parameter can be automatically adjusted by the self-organizing learning algorithm; the self-organizing learning algorithm used is not the same as the currently reported document, Which is a new self-organizing learning algorithm based on the linear self-regression smoothing model of the multi-modal vibration system of the photoelectric laminated structure. In order to verify the effectiveness of the proposed intelligent active control algorithm, the multi-modal active control example of the plate-shell structure is simulated.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：TB535.1

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4 劉沛;王學(xué)智;;模糊控制的一種方便實(shí)現(xiàn)[A];1996年中國(guó)智能自動(dòng)化學(xué)術(shù)會(huì)議論文集（上冊(cè)）[C];1996年

5 袁鑄鋼;江海鷹;王孝紅;孟慶金;高云深;;水泥立窯偏火的模糊控制[A];1996年中國(guó)智能自動(dòng)化學(xué)術(shù)會(huì)議論文集（上冊(cè)）[C];1996年

6 柴景武;王建農(nóng);;鍋爐溫度的模糊控制[A];1998年中國(guó)智能自動(dòng)化學(xué)術(shù)會(huì)議論文集（上冊(cè)）[C];1998年

7 孫烈英;;模糊控制過(guò)程中參數(shù)自調(diào)整的方法[A];1995中國(guó)控制與決策學(xué)術(shù)年會(huì)論文集[C];1995年

8 章海清;吳慶憲;;提高模糊控制品質(zhì)的一種方法[A];第三屆全國(guó)控制與決策系統(tǒng)學(xué)術(shù)會(huì)議論文集[C];1991年

9 劉向杰;彭一民;周孝信;;模糊控制在熱工控制中的應(yīng)用現(xiàn)狀及前景[A];1998年中國(guó)控制會(huì)議論文集[C];1998年

10 盛萬(wàn)興;戴汝為;;模糊控制的集成優(yōu)化[A];1997中國(guó)控制與決策學(xué)術(shù)年會(huì)論文集[C];1997年

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