Rotation Angle Control Strategy for Telescopic Flexible Manipulator Based on a Combination of Fuzzy Adjustment and RBF Neural Network

作     者：Dongyang Shang Xiaopeng Li Meng Yin Fanjie Li Bangchun Wen Dongyang Shang;Xiaopeng Li;Meng Yin;Fanjie Li;Bangchun Wen

作者机构：School of Mechanical Engineering and AutomationNortheastern UniversityShenyang 110819China Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China 

出 版 物：《Chinese Journal of Mechanical Engineering》 (中国机械工程学报（英文版）)

年 卷 期：2022年第35卷第4期

页      面：203-226页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：Supported by National Natural Science Foundation of China(Grant No.51875092) National Key Research and Development Project of China(Grant No.2020YFB2007802) Natural Science Foundation of Ningxia Province(Grant No.2020AAC03279) Fundamental Research Funds for the Central Universities(Grant No.N2103025) 

主　　题：Flexible manipulator RBF neural network Fuzzy control Dynamic uncertainty 

摘      要：The length of fexible manipulators with a telescopic arm alters during *** dynamic parameters of telescopic fexible manipulators exhibit signifcant time-varying characteristics owing to variations in *** an increase in the manipulators’length,the nonlinear terms caused by fexibility in the manipulators’dynamic equations cannot be *** time-varying characteristics and nonlinear terms of telescopic fexible manipulators cause fuctuations in rotation angles,which afect the operation accuracy of *** this study,a control strategy based on a combination of fuzzy adjustment and an RBF neural network is utilized to improve the control accuracy of fexible telescopic ***,the dynamic equation of the manipulators is established using the assumed mode method and Lagrange’s principle,and the infuence of nonlinear terms is ***,a combined control strategy is proposed to suppress the fuctuation of the rotation angle in telescopic fexible *** variation ranges of the feedforward PD controller parameters are determined by the pole placement strategy and length of the *** rules are utilized to adjust the controller parameters in *** RBF neural network is utilized to identify and compensate the uncertain part of the dynamic model of the fexible *** uncertain part comprises time-varying parameters and nonlinear ***,numerical simulations and prototype experiments prove the efectiveness of the combined control *** results prove that the proposed control strategy has a smaller standard deviation of ***,the combined control strategy is more suitable for telescopic fexible manipulators,which can efectively improve the control accuracy of rotation angles.