發(fā)布時(shí)間：2018-01-15 09:36

  本文關(guān)鍵詞：基于RBF神經(jīng)網(wǎng)絡(luò)的自抗擾控制器的三電機(jī)同步控制系統(tǒng)　出處：《江蘇大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 解耦控制 自抗擾控制器 徑向基函數(shù)神經(jīng)網(wǎng)絡(luò) 參數(shù)自調(diào)節(jié) PLC

【摘要】：隨著科技的飛速發(fā)展,工業(yè)自動(dòng)化已逐漸滲透到各行各業(yè),速度、效率、性能以及經(jīng)濟(jì)高的多電機(jī)控制成為研究和討論的熱點(diǎn),本文針對三電機(jī)同步控制的控制器參數(shù)優(yōu)化問題以及解耦問題進(jìn)行了一系列探討。在分析三電機(jī)同步控制模型的基礎(chǔ)上,因傳統(tǒng)比例-積分-微分(Prorportion Integration Differentiation,PID)控制方案的不足,根據(jù)自抗擾控制器(Active Disturbance Rejection Controller,ADRC)不依賴于系統(tǒng)具體模型且可將外部擾動(dòng)和內(nèi)部擾動(dòng)作為一個(gè)總擾動(dòng)進(jìn)行觀測與補(bǔ)償?shù)膬?yōu)勢,提出了基于一階ADRC的三電機(jī)同步控制策略解決張力與速度的解耦問題,并將其與PID控制性能進(jìn)行仿真實(shí)驗(yàn)對比,驗(yàn)證了ADRC在調(diào)節(jié)速度、超調(diào)量和抗干擾等方面的優(yōu)異性。其次針對ADRC算法復(fù)雜、參數(shù)多且調(diào)節(jié)耗時(shí)耗力的問題,對ADRC結(jié)構(gòu)進(jìn)行優(yōu)化的基礎(chǔ)上,提出基于徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)(Radical Basis Function Neural Network,RBFNN)的自適應(yīng)ADRC來解決參數(shù)優(yōu)化問題。該控制器利用RBFNN能以任意精度逼近任意非線性函數(shù)能力以及結(jié)構(gòu)簡單等特點(diǎn),實(shí)現(xiàn)對被控對象的在線跟蹤,獲得ADRC參數(shù)的在線調(diào)整信息,從而實(shí)現(xiàn)ADRC參數(shù)的在線調(diào)節(jié)功能。本文控制系統(tǒng)以可編程邏輯控制器(Programmable Logic Controller,PLC)S7-300作為控制器,通過PROFINET通訊方式上連臺式機(jī),完成硬件組態(tài)、程序的下載、調(diào)試等功能,同時(shí)在視窗控制中心(Windows Control Center,WinCC)組態(tài)軟件中設(shè)計(jì)遠(yuǎn)程監(jiān)控系統(tǒng),實(shí)時(shí)監(jiān)控控制過程變化。通過PROFIBUS-DP通訊方式下連變頻器,完成主從方式通訊網(wǎng)絡(luò)的構(gòu)建。ADRC、參數(shù)采集、通訊、RBFNN等程序的編寫采用結(jié)構(gòu)化編程的方法在上位機(jī)博途V13軟件中完成。在搭建的實(shí)驗(yàn)平臺上,進(jìn)行基于RBFNN的自適應(yīng)ADRC的階躍響應(yīng)實(shí)驗(yàn)、解耦實(shí)驗(yàn)以及抗負(fù)載實(shí)驗(yàn),驗(yàn)證了RBFNN在非線性映射方面的優(yōu)勢,表明該控制器不僅能實(shí)現(xiàn)部分參數(shù)的自調(diào)節(jié)功能,而且可降低系統(tǒng)超調(diào)量,提高系統(tǒng)的動(dòng)態(tài)性能和穩(wěn)態(tài)精度。實(shí)驗(yàn)結(jié)果證明該方法具有實(shí)際應(yīng)用性。
[Abstract]:With the rapid development of science and technology, industrial automation has gradually penetrated into all walks of life, speed, efficiency, performance and high economic hot multi motor control can be studied and discussed in this paper, three motor synchronous control of controller parameters optimization and decoupling problem undertook a series of study. Based on synchronization control model in the analysis of three of the motor. Because of the traditional proportional integral differential (Prorportion Integration Differentiation, PID) lack of control scheme, based on ADRC (Active Disturbance Rejection Controller, ADRC) do not depend on the specific model and the external disturbance and the internal disturbance as a general disturbance observation and compensation advantages, proposed synchronous decoupling control problem strategies to solve the tension and speed of the three motor order based on ADRC, and the PID control performance simulation comparison, verification The ADRC in the regulation of speed, excellent overshoot and anti-jamming. Secondly, ADRC algorithm is complex, multiple parameters and the adjusting time-consuming problem, optimization based on ADRC structure, is proposed based on radial basis function neural network (Radical Basis Function Neural Network, RBFNN ADRC) to solve the adaptive a parameter optimization problem. The controller uses RBFNN can approximate any nonlinear function ability and simple structure with arbitrary precision, realize the online tracking of the controlled object, on-line tuning information of ADRC parameters obtained online, so as to realize the ADRC parameter adjustment function. The control system based on programmable logic controller (Programmable Logic, Controller, PLC) S7-300 as a controller, through the PROFINET communication way connected on the desktop, complete the hardware configuration, program download, debugging and other functions, at the same time in the windows control center (Wind Ows Control Center, WinCC) remote monitoring system design of configuration software, real-time monitoring and control of process changes. Through PROFIBUS-DP communication even under the inverter, completes the construction of master-slave communication networks.ADRC, data acquisition, communication, preparation method adopts the structure of programming RBFNN program in PC botu V13 software. The experimental platform, adaptive ADRC RBFNN step response experiment based on decoupling experiment and anti load experiment, verify the advantages of RBFNN in nonlinear mapping, show that the self regulating function of the controller can not only achieve some parameters, but also can reduce overshoot and improve the system dynamic performance and steady-state precision experimental results show that this method has practical applications.

【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP183;TP273;TM301.2

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本文編號：1427760