本文選題：遲滯 + 極限學(xué)習(xí)機(jī)�。� 參考：《浙江理工大學(xué)》2017年碩士論文

【摘要】：隨著微納米技術(shù)的快速發(fā)展,高精度定位系統(tǒng)也隨之成為研究的熱點(diǎn)。壓電陶瓷、形狀記憶合金等智能材料由于具有響應(yīng)速度快、定位精度高、穩(wěn)定性好等優(yōu)點(diǎn),被廣泛應(yīng)用于高精度定位領(lǐng)域中。然而這些智能材料存在固有的遲滯非線性特性,影響了系統(tǒng)的表現(xiàn),造成系統(tǒng)的控制精度降低和穩(wěn)定性能變差。為了消除遲滯對(duì)高精度定位系統(tǒng)造成的不良影響,使系統(tǒng)能更好地運(yùn)行,需要對(duì)智能材料中的遲滯非線性進(jìn)行建模,并對(duì)其設(shè)計(jì)相應(yīng)的控制器進(jìn)行有效控制。論文的主要研究工作如下:(1)建立了基于Duhem算子的極限學(xué)習(xí)機(jī)遲滯模型。首先,引入Duhem基本遲滯算子將遲滯的多值映射轉(zhuǎn)化為一對(duì)一的映射關(guān)系,然后應(yīng)用極限學(xué)習(xí)機(jī)逼近遲滯關(guān)系并進(jìn)行訓(xùn)練辨識(shí),最后通過對(duì)實(shí)際的壓電執(zhí)行器進(jìn)行遲滯建模驗(yàn)證該建模方法的有效性,仿真結(jié)果表明該方法能夠準(zhǔn)確地建立遲滯模型。(2)設(shè)計(jì)了遲滯非線性系統(tǒng)預(yù)設(shè)性能自適應(yīng)滑模反步控制器。針對(duì)一類含有遲滯的非線性系統(tǒng),應(yīng)用Backlash-like模型來描述系統(tǒng)中存在的遲滯特性。首先介紹了Backlash-like模型的數(shù)學(xué)表達(dá)式以及分析了該模型中的類擾動(dòng)變量,利用徑向基神經(jīng)網(wǎng)絡(luò)對(duì)該類擾動(dòng)項(xiàng)進(jìn)行近似逼近。然后引入預(yù)設(shè)性能函數(shù)對(duì)遲滯非線性系統(tǒng)進(jìn)行誤差變換,并結(jié)合滑模反步法以及Lyapunov穩(wěn)定理論,為該系統(tǒng)設(shè)計(jì)了自適應(yīng)滑模反步控制器。最后通過仿真進(jìn)行了驗(yàn)證。(3)設(shè)計(jì)了輸入受限遲滯非線性系統(tǒng)的自適應(yīng)滑�？刂破��？紤]一類含有Backlash-like遲滯特性的輸入受限非線性系統(tǒng),首先利用徑向基神經(jīng)網(wǎng)絡(luò)對(duì)遲滯模型中的類擾動(dòng)項(xiàng)進(jìn)行近似逼近,然后通過定義一個(gè)穩(wěn)定自適應(yīng)的輔助補(bǔ)償系統(tǒng),采用輸入飽和誤差動(dòng)態(tài)放大的方法來實(shí)現(xiàn)控制飽和的補(bǔ)償,最后結(jié)合Lyapunov函數(shù)為該系統(tǒng)完成自適應(yīng)滑�？刂破髟O(shè)計(jì)。該方法考慮了控制輸入受限,符合實(shí)際工程情況,有效減少了智能材料中遲滯非線性對(duì)系統(tǒng)造成的不良影響,提高了系統(tǒng)的控制精度和穩(wěn)定性能。仿真結(jié)果表明該控制方法有效。
[Abstract]:With the rapid development of micro-and nano-technology, high-precision positioning system has become a hot spot.Piezoelectric ceramics and shape memory alloy (SMA) have been widely used in the field of high precision positioning due to their high response speed, high positioning accuracy and good stability.However, these intelligent materials have inherent hysteresis nonlinear characteristics, which affect the performance of the system, resulting in the reduction of the control accuracy and the deterioration of the stability of the system.In order to eliminate the bad effect of hysteresis on high precision positioning system and make the system work better, it is necessary to model the hysteresis nonlinearity in intelligent materials and control the corresponding controller effectively.The main research work of this paper is as follows: (1) A hysteresis model of extreme learning machine based on Duhem operator is established.Firstly, the Duhem basic hysteresis operator is introduced to transform the multi-valued mapping of hysteresis into one-to-one mapping relationship, then the hysteresis relation is approximated by the extreme learning machine and the training identification is carried out.Finally, the effectiveness of the modeling method is verified by the hysteresis modeling of the practical piezoelectric actuator. The simulation results show that the proposed method can accurately establish the hysteresis model. 2) an adaptive sliding mode backstepping controller with preset performance for a hysteresis nonlinear system is designed.For a class of nonlinear systems with hysteresis, the Backlash-like model is applied to describe the hysteresis characteristics of the system.Firstly, the mathematical expression of Backlash-like model is introduced, and the similar perturbation variables in the model are analyzed. The radial basis function neural network is used to approximate the disturbance term.Then an adaptive sliding mode backstepping controller is designed for the hysteretic nonlinear system based on the sliding mode backstepping method and Lyapunov stability theory.Finally, an adaptive sliding mode controller for input constrained hysteretic nonlinear systems is designed and verified by simulation.In this paper, a class of input-constrained nonlinear systems with Backlash-like hysteresis characteristics is considered. First, the radial basis function neural network is used to approximate the perturbation term in the hysteresis model, and then a stable adaptive auxiliary compensation system is defined.The method of dynamic amplification of input saturation error is used to compensate the control saturation. Finally, the adaptive sliding mode controller is designed for the system with Lyapunov function.This method takes into account the limited control input and accords with the actual engineering conditions. It can effectively reduce the adverse effects of hysteresis nonlinearity on the system in intelligent materials and improve the control accuracy and stability performance of the system.Simulation results show that the control method is effective.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP273

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