基于神經網絡PID的挖掘機軌跡控制系統(tǒng)的實驗研究
發(fā)布時間：2018-11-05 10:53
【摘要】：挖掘機以其高性能、高效率等優(yōu)點在建筑領域得到了廣泛地應用,但是在目前挖掘機作業(yè)過程中,絕大多數依靠駕駛員手動操作,且不宜在危險環(huán)境或者長時間工作,實現挖掘機的自動化與智能化顯得尤為重要。本課題以實驗室挖掘機實驗平臺為研究對象,旨在通過設計一種高性能的控制器來實現挖掘機工作裝置軌跡的高性能控制。本文研究內容主要包括挖掘機工裝軌跡控制系統(tǒng)的建模,控制器的設計仿真以及實驗研究。在挖掘機控制系統(tǒng)建模過程中,對于動臂和斗桿控制系統(tǒng)分別進行建模,計算各環(huán)節(jié)的傳遞函數。液壓系統(tǒng)動力機構環(huán)節(jié)對于液壓缸活塞正反向運動分別進行建模,機械結構環(huán)節(jié)采用最小二乘法擬合求取其傳遞函數。為實現數字計算機控制,對連續(xù)傳遞函數進行離散化,求取其脈沖傳遞函數和差分方程�；诮⒌耐诰驒C工裝軌跡控制系統(tǒng)的離散數學模型和增量式PID控制算法,設計基于普通PID控制器的變速積分數字PID控制器和由神經網絡實現控制器參數調節(jié)的單神經元自適應PID控制器、BP神經網絡PID控制器,并在MATLAB軟件中編寫控制器的控制算法程序,完成控制器的參數整定及仿真,得到動臂控制系統(tǒng)和斗桿控制系統(tǒng)在各個控制器作用下,對于測試信號的響應曲線及PID控制器參數變化曲線的仿真結果�；趯嶒炇彝诰驒C實驗平臺,編寫各控制算法的控制程序,進行相關的實驗研究,將普通PID控制器,變速積分PID控制器、單神經元自適應PID控制器以及BP神經網絡PID控制器的實驗結果進行分析和比較。實驗結果表明,基于神經網絡的單神經元自適應PID控制器和BP神經網絡PID控制器控制效果比常規(guī)PID控制器控制效果要好,適應性更強,而兩者中BP神經網絡PID控制效果更佳,具有很好的應用前景。
[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.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TU621;TP183;TP273
，
本文編號：2311842
[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.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TU621;TP183;TP273
，
本文編號：2311842
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