本文選題：遲滯非線性　切入點(diǎn)：率相關(guān)性　出處：《西南交通大學(xué)》2017年碩士論文

【摘要】：作為典型的智能結(jié)構(gòu),壓電作動(dòng)器和超磁致作動(dòng)器獲得了廣泛應(yīng)用。但是作動(dòng)器存在復(fù)雜的率相關(guān)遲滯非線性,會(huì)造成系統(tǒng)精度超差,易產(chǎn)生振蕩,甚至閉環(huán)系統(tǒng)不穩(wěn)定等問題。如何對(duì)智能結(jié)構(gòu)進(jìn)行建模和控制,具有重要的理論研究意義和工程應(yīng)用價(jià)值。本文以壓電作動(dòng)器和超磁致作動(dòng)器為控制對(duì)象,深入研究率相關(guān)遲滯非線性系統(tǒng)的逆補(bǔ)償控制理論與方法,旨在消除率相關(guān)遲滯非線性對(duì)控制精度的影響,分別實(shí)現(xiàn)壓電作動(dòng)器和超磁致作動(dòng)器的實(shí)時(shí)跟蹤控制。文章從遲滯非線性系統(tǒng)建模、逆補(bǔ)償控制策略設(shè)計(jì)、實(shí)驗(yàn)驗(yàn)證三個(gè)層次展開,主要研究內(nèi)容如下:采用Hammerstein模型思想分別建立壓電作動(dòng)器和超磁致作動(dòng)器的遲滯模型。采用BP神經(jīng)網(wǎng)絡(luò)和ARX模型分別表征作動(dòng)器的遲滯非線性和率相關(guān)性。采用擴(kuò)展空間輸入法,以神經(jīng)網(wǎng)絡(luò)結(jié)合play算子構(gòu)建的基本遲滯算子,來克服作動(dòng)器的多值映射性。建模結(jié)果表明,無論是壓電作動(dòng)器還是超磁致作動(dòng)器,所設(shè)計(jì)的模型都能描述其遲滯非線性,而且具有易于辨識(shí)、頻率泛化能力強(qiáng)等優(yōu)點(diǎn)。設(shè)計(jì)了前饋反饋復(fù)合控制策略對(duì)壓電作動(dòng)器和超磁致作動(dòng)器進(jìn)行跟蹤控制。采用作動(dòng)器的Hammerstein逆模型構(gòu)建前饋控制器,分別設(shè)計(jì)了 PID控制、基于單神經(jīng)元PID控制、模糊PD控制三種反饋控制器。搭建了基于dSPACE半實(shí)物仿真平臺(tái)的作動(dòng)器實(shí)時(shí)跟蹤控制實(shí)驗(yàn)系統(tǒng),介紹了實(shí)驗(yàn)流程和S-Function的編寫方法。對(duì)壓電作動(dòng)器和超磁致作動(dòng)器,分別設(shè)計(jì)了實(shí)時(shí)跟蹤控制實(shí)驗(yàn)。對(duì)實(shí)驗(yàn)結(jié)果進(jìn)行了分析和比較。跟蹤結(jié)果表明,對(duì)壓電作動(dòng)器和超磁致作動(dòng)器,所設(shè)計(jì)的復(fù)合控制策略都能有效跟蹤,能夠滿足工程和研究需要。
[Abstract]:As a typical intelligent structure, piezoelectric actuators and giant magnetic actuators have been widely used.However, the actuator has complex rate dependent hysteresis nonlinearity, which will lead to the system accuracy is too poor, easy to produce oscillations, even closed-loop system instability and so on.How to model and control intelligent structures has important theoretical significance and engineering application value.Taking piezoelectric actuator and giant magnetic actuator as control objects, the inverse compensation control theory and method for rate-dependent hysteresis nonlinear systems are studied in this paper, in order to eliminate the influence of rate-dependent hysteresis nonlinearity on control accuracy.The real-time tracking control of piezoelectric actuator and giant magnetic actuator is realized respectively.In this paper, three levels of hysteresis nonlinear system modeling, inverse compensation control strategy design and experimental verification are developed. The main research contents are as follows: the hysteresis models of piezoelectric actuator and giant magnetic actuator are established by using Hammerstein model.The hysteresis nonlinearity and rate correlation of actuator are characterized by BP neural network and ARX model, respectively.The extended space input method and the basic hysteresis operator constructed by neural network combined with play operator are used to overcome the multi-valued mapping of actuators.The modeling results show that both the piezoelectric actuator and the giant magnetic actuator can describe the hysteresis nonlinearity and have the advantages of easy identification and strong frequency generalization.A feedforward and feedback compound control strategy is designed to track and control piezoelectric actuators and giant magnetic actuators.The feedforward controller is constructed by using the Hammerstein inverse model of actuator. Three kinds of feedback controllers are designed: PID control, single neuron PID control and fuzzy PD control.A real-time tracking control experiment system of actuators based on dSPACE hardware-in-the-loop simulation platform is built. The experimental flow and the programming method of S-Function are introduced.Real-time tracking control experiments are designed for piezoelectric actuators and giant magnetic actuators.The experimental results are analyzed and compared.The tracking results show that both the piezoelectric actuator and the Giant Magneto-Actuator can be tracked effectively and can meet the needs of engineering and research.
【學(xué)位授予單位】：西南交通大學(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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