RBF Parameter Identification of Valve-Controlled Cylinder System for Excavator Robot

Abstract

To improve the planned control accuracy of trajectory of the working parts of a hydraulic excavator robot,i.e.,to develop an applicable control model for the valve-controlled cylinder system instead of the ideal one so as to make the control of the robot more actual with control error decreased.By way of RBF neural network,a set of nonlinear equations was deduced involving the parameters to be identified for the valve-controlled cylinder system and relevant Jacobian information.With the bucket arm/cylinder system of the excavator robot investigated via tests,such parameters of cylinder's oil inlet/outlet pressure and rake angle of bucket arm were given to identify the valve's gain coefficient kq,oil volumetric coefficient Eoil and inner leak coefficient Cliin the valve-controlled cylinder model.It is verified that the model involving identification parameters has high control accuracy and robustness as the result of a comparative force control test for the valve-controlled cylinder system.