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Parameter identification of servo system for excavator based on DRNN

LI Bo,YAN Jun,GUO Gang,QIAN Haibo,ZHANG Meijun(College of Field Engineering,PLA Univ.of Sci.& Tech.,Nanjing 210007,China)  
To analyze the servo system of the excavator effectively and design the controller precisely,the state space model was built.According to the characteristics of the parameter uncertainties,an identification method based on diagonal recurrent neural network(DRNN) was developed.The Jacobian information was achieved by on-line learning through neural networks,and least square method was proposed to identify the unknown parameters by using the achieved information.The comparison experiment demonstrated that the identified model can approximate actual system gradually,and that the proposed method in this paper can meet the need for identifying the unknown parameters.
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