摘要
搭建了气动人工肌肉静态测试平台,在0.1~0.8 MPa气压下进行多次测量试验,对气动人工肌肉进行特性分析,根据理论模型和测试数据建立了数学模型,模型求解精度较好。考虑外负载、气源气压和系统摩擦等因素对数学模型的影响,结合RBF网络的快速学习能力设计了一种基于RBF网络的PID控制策略。在外负载50~200 N的条件下,搭建了气动人工肌肉动态测试平台并进行了多组位置控制试验。结果表明,传统PID控制只能在一定的外负载范围内实现较好的位置控制,基于RBF网络的PID控制能自适应调整PID参数,且响应速度快,调节时间短,超调量小,能更好地补偿其数学模型误差并实现精确的位置控制。
The static test platform of pneumatic artificial muscle was built,and a series of measurement tests were carried out under pressure of 0.1~0.8 MPa to analyze the characteristics of pneumatic artificial muscle.The mathematical model,which was built based on the theoretical model and test data,shows a high accuracy of solution.In consideration of the influence of external load,gas pressure and system friction on the mathematical model,a PID control strategy based on RBF network was designed with the fast learning ability of RBF network.Under the condition of external load F=50~200 N,the dynamic test platform was built and a number of position control tests were implemented.The results show that the traditional PID control strategy can only achieve better control accuracy within a certain range of external loads,while the proposed strategy is able to adjust the PID parameters adaptively.Moreover,the proposed PID control strategy has the advantages of higher response speed,shorter adjustment time and smaller overshoot,and it can better compensate the mathematical model error and achieve higher control accuracy.
作者
刘凯
陈伊宁
吴阳
王扬威
LIU Kai;CHEN Yining;WU Yang;WANG Yangwei(College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,Jiangsu,China;College of Mechanical and Electrical Engineering,Northeast Forestry University,Harbin 150040,Heilongjiang,China)
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2020年第5期142-148,共7页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(51405229)
江苏省自然科学基金资助项目(BK20151470,BK20171416)。
