Authors: Chen Liu Shengdun Zhao Xiaolan Han Tong Guo
Publish Date: 2017/02/19
Volume: 31, Issue: 2, Pages: 903-910
Abstract
This paper focuses on the quality improvements on clinching joints using a servo press with a Radial basis function neural network and a sliding mode RBFS control strategy Bottom thickness which is affected by the press punch position is usually used to monitor clinching joint quality Traditional clinching presses are driven by pneumatic pistons or motors that provide feedback on punch force or motor position However this feedback is indirectly related to the joint bottom thickness Clinching workers who set the control parameters on these presses depend on tests and statistics Thus this paper presents a servo press system that utilizes punch position feedback to directly control the joint bottom thickness Transmission errors are considered for the movement accuracy of the servo press A mathematical model of the servo press is established for analyzing An algorithm which combines RBF neural network and sliding mode is proposed and applied for press position tracking This algorithm adopts an RBF neural network to approximate the nominal model of the press system The update law of the algorithm is based on the Lyapunov function used to prove the stability of a closedloop system The sliding mode controller compensates for the neural network error and disturbance Finally experiments are executed on the servo press with an RBFS controller To evaluate the performance of the proposed method a fuzzy PID controller is also applied to the press for comparison The results indicate that the servo clinching press system with RBFS efficiently and accurately control the clinching jointing processChen Liu received his BS degree 2010 from the Xian University of Technology and ME degree 2012 from Xi’an Jiaotong University He is currently a doctoral candidate at the School of Mechanical Engineering at Xi’an Jiaotong University His major areas of interest are servo control technology and parallel robots
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