【摘要】：伺服加載系統(tǒng)是一種重要的半實物仿真設(shè)備,它為研究伺服系統(tǒng)的控制性能提供精確可靠的實驗數(shù)據(jù),同時能夠節(jié)約研發(fā)經(jīng)費和縮短研發(fā)周期。20多年來,伺服加載技術(shù)的研究一直是軍事、國防和航空航天等工業(yè)領(lǐng)域中的重要課題。本文結(jié)合某伺服系統(tǒng)加載測試設(shè)備研制的實際項目,對電動伺服加載系統(tǒng)進行了詳細的理論研究并完成系統(tǒng)的硬件設(shè)計和軟件設(shè)計。首先,建立了電動伺服加載系統(tǒng)的數(shù)學(xué)模型,分析了多余力矩產(chǎn)生的根本原因。在基于結(jié)構(gòu)不變性原理的基礎(chǔ)上設(shè)計了相應(yīng)的前饋補償控制器以削弱系統(tǒng)產(chǎn)生的多余力矩。其次,針對傳統(tǒng)PID控制器存在的不足,鑒于BP神經(jīng)網(wǎng)絡(luò)具有非線性、并行分布處理、算法實現(xiàn)容易、自學(xué)習(xí)和自適應(yīng)等優(yōu)點,將BP神經(jīng)網(wǎng)絡(luò)智能算法引入加載系統(tǒng)并設(shè)計了 BP+PID復(fù)合控制器。該復(fù)合控制器結(jié)合了傳統(tǒng)PID控制與BP神經(jīng)網(wǎng)絡(luò)的優(yōu)點,經(jīng)仿真證明,該控制器相比于常規(guī)PID控制器能大幅度提高加載精度。然后,分析了加載系統(tǒng)的硬件結(jié)構(gòu),從系統(tǒng)的性能指標要求出發(fā)介紹了系統(tǒng)控制模塊、采集模塊、檢測模塊、通信模塊以及執(zhí)行機構(gòu)的選型依據(jù),并在此基礎(chǔ)上采用虛擬儀器技術(shù)完成了電動加載系統(tǒng)的硬件設(shè)計。最后,在所設(shè)計的硬件架構(gòu)基礎(chǔ)上,利用Labview編寫相對應(yīng)的軟件,完成了軟件各功能模塊的設(shè)計。通過對實際系統(tǒng)進行的一系列的調(diào)試,驗證了所設(shè)計的電動加載系統(tǒng)的可行性。
[Abstract]:Servo loading system is an important hardware-in-the-loop simulation equipment. It provides accurate and reliable experimental data for studying the control performance of servo system, and can save R & D expenses and shorten R & D cycle for more than 20 years. Servo loading technology has been an important subject in military, national defense and aerospace industries. Combining with the actual project of a servo system load test equipment, this paper makes a detailed theoretical study on the electric servo loading system and completes the hardware and software design of the system. Firstly, the mathematical model of electric servo loading system is established, and the root cause of excess torque is analyzed. Based on the principle of structural invariance, the corresponding feedforward compensation controller is designed to reduce the excess torque generated by the system. Secondly, in view of the disadvantages of traditional PID controller, considering the advantages of BP neural network, such as nonlinear, parallel distributed processing, easy implementation of algorithm, self-learning and adaptive, etc. The intelligent algorithm of BP neural network is introduced into the loading system and the BP PID compound controller is designed. The composite controller combines the advantages of traditional PID control and BP neural network. The simulation results show that the controller can greatly improve the loading accuracy compared with the conventional PID controller. Then, the hardware structure of the loading system is analyzed, and the system control module, the acquisition module, the detection module, the communication module and the type selection basis of the executive mechanism are introduced according to the performance requirements of the system. On this basis, the hardware design of electric loading system is completed by using virtual instrument technology. Finally, on the basis of the designed hardware architecture, the corresponding software is written by using Labview, and the design of each function module of the software is completed. Through a series of debugging to the actual system, the feasibility of the designed electric loading system is verified.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM921.541

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