【摘要】：挖掘機(jī)以其高性能、高效率等優(yōu)點(diǎn)在建筑領(lǐng)域得到了廣泛地應(yīng)用,但是在目前挖掘機(jī)作業(yè)過(guò)程中,絕大多數(shù)依靠駕駛員手動(dòng)操作,且不宜在危險(xiǎn)環(huán)境或者長(zhǎng)時(shí)間工作,實(shí)現(xiàn)挖掘機(jī)的自動(dòng)化與智能化顯得尤為重要。本課題以實(shí)驗(yàn)室挖掘機(jī)實(shí)驗(yàn)平臺(tái)為研究對(duì)象,旨在通過(guò)設(shè)計(jì)一種高性能的控制器來(lái)實(shí)現(xiàn)挖掘機(jī)工作裝置軌跡的高性能控制。本文研究?jī)?nèi)容主要包括挖掘機(jī)工裝軌跡控制系統(tǒng)的建模,控制器的設(shè)計(jì)仿真以及實(shí)驗(yàn)研究。在挖掘機(jī)控制系統(tǒng)建模過(guò)程中,對(duì)于動(dòng)臂和斗桿控制系統(tǒng)分別進(jìn)行建模,計(jì)算各環(huán)節(jié)的傳遞函數(shù)。液壓系統(tǒng)動(dòng)力機(jī)構(gòu)環(huán)節(jié)對(duì)于液壓缸活塞正反向運(yùn)動(dòng)分別進(jìn)行建模,機(jī)械結(jié)構(gòu)環(huán)節(jié)采用最小二乘法擬合求取其傳遞函數(shù)。為實(shí)現(xiàn)數(shù)字計(jì)算機(jī)控制,對(duì)連續(xù)傳遞函數(shù)進(jìn)行離散化,求取其脈沖傳遞函數(shù)和差分方程�；诮⒌耐诰驒C(jī)工裝軌跡控制系統(tǒng)的離散數(shù)學(xué)模型和增量式PID控制算法,設(shè)計(jì)基于普通PID控制器的變速積分?jǐn)?shù)字PID控制器和由神經(jīng)網(wǎng)絡(luò)實(shí)現(xiàn)控制器參數(shù)調(diào)節(jié)的單神經(jīng)元自適應(yīng)PID控制器、BP神經(jīng)網(wǎng)絡(luò)PID控制器,并在MATLAB軟件中編寫(xiě)控制器的控制算法程序,完成控制器的參數(shù)整定及仿真,得到動(dòng)臂控制系統(tǒng)和斗桿控制系統(tǒng)在各個(gè)控制器作用下,對(duì)于測(cè)試信號(hào)的響應(yīng)曲線及PID控制器參數(shù)變化曲線的仿真結(jié)果�；趯�(shí)驗(yàn)室挖掘機(jī)實(shí)驗(yàn)平臺(tái),編寫(xiě)各控制算法的控制程序,進(jìn)行相關(guān)的實(shí)驗(yàn)研究,將普通PID控制器,變速積分PID控制器、單神經(jīng)元自適應(yīng)PID控制器以及BP神經(jīng)網(wǎng)絡(luò)PID控制器的實(shí)驗(yàn)結(jié)果進(jìn)行分析和比較。實(shí)驗(yàn)結(jié)果表明,基于神經(jīng)網(wǎng)絡(luò)的單神經(jīng)元自適應(yīng)PID控制器和BP神經(jīng)網(wǎng)絡(luò)PID控制器控制效果比常規(guī)PID控制器控制效果要好,適應(yīng)性更強(qiáng),而兩者中BP神經(jīng)網(wǎng)絡(luò)PID控制效果更佳,具有很好的應(yīng)用前景。
[Abstract]:The excavator has been widely used in the field of construction because of its high performance and high efficiency. However, most of the excavators depend on manual operation of the excavator in the current operation process, and it is not suitable to work in dangerous environment or for a long time. It is very important to realize the automation and intelligence of excavator. Taking the experimental platform of laboratory excavator as the research object, the purpose of this paper is to design a kind of high performance controller to realize the high performance control of the track of the excavator's working device. This paper mainly includes the modeling of excavator tool trajectory control system, the design and simulation of controller and the experimental research. In the process of modeling the control system of excavator, the control system of moving arm and bucket rod is modeled separately, and the transfer function of each link is calculated. The dynamic mechanism of hydraulic system models the forward and backward movement of piston in hydraulic cylinder, and the transfer function is obtained by least square fitting in mechanical structure. In order to realize digital computer control, the continuous transfer function is discretized and its pulse transfer function and difference equation are obtained. Based on the discrete mathematical model and incremental PID control algorithm of excavator tooling trajectory control system, The variable speed integral digital PID controller based on ordinary PID controller and the single neuron adaptive PID controller and BP neural network PID controller are designed. The control algorithm program of the controller is written in the MATLAB software, the parameters of the controller are set and simulated, and the control system of the moving arm and the bucket rod control system are obtained under the action of each controller. The simulation results of the response curve of the test signal and the parameter change curve of the PID controller are given. Based on the experimental platform of the laboratory excavator, the control program of each control algorithm is compiled, and the related experimental research is carried out. The general PID controller and the variable speed integral PID controller are used. The experimental results of single neuron adaptive PID controller and BP neural network PID controller are analyzed and compared. The experimental results show that the control effect of single neuron adaptive PID controller and BP neural network PID controller based on neural network is better than that of conventional PID controller, and the BP neural network PID control effect is better than that of conventional PID controller. It has a good application prospect.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TU621;TP183;TP273
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本文編號(hào)：2311842