基于RBF神经网络的固定时间滑模控制策略研究  

Research on fixed time sliding mode control strategy based on RBF neural network

作　　者：张鑫[1,2] 权莹 ZHANG Xin;QUAN Ying(School of Automation&Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China;Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics&Image Processing,Lanzhou 730070,China)

机构地区：[1]兰州交通大学自动化与电气工程学院,甘肃兰州730070 [2]甘肃省人工智能与图形图像处理工程研究中心,甘肃兰州730070

出　　处：《Journal of Measurement Science and Instrumentation》2023年第2期218-225,共8页测试科学与仪器（英文版）

基　　金：Natural Science Foundation of Gansu Province(No.20JR5RA419);Lanzhou Jiaotong University-Tianjin University Innovation Fund Project(No.2019053)。

摘　　要：为了实现对机械臂末端的高精度跟踪控制,本文提出了一种基于径向基函数(Radial basis function,RBF)神经网络的固定时间滑模跟踪控制策略。首先,建立机械臂的动力学模型。然后,将RBF神经网络和固定时间滑模面结合,设计RBF固定时间滑模控制器,以实现对机械臂末端轨迹的高精度控制;并利用Lyapunov稳定性理论对所设计控制器的理论可行性进行了证明。最后,以二关节机械臂为研究对象进行仿真实验。结果表明:RBF神经网络的固定时间滑模跟踪控制策略能估计模型中的不确定参数,有效地改善了控制效果;并使控制器具有固定时间收敛特性,提高了机械臂的收敛速度。In order to achieve high-precision tracking control of the end of manipulator,a fixed-time sliding mode tracking control strategy based on radial basis function(RBF)neural network is proposed.First,the dynamic model of the manipulator is established.Then the RBF neural network is combined with the fixed-time sliding mode surface to design the RBF fixed-time sliding mode controller to achieve high-precision control of the end trajectory of the manipulator.And the theoretical feasibility of the designed controller is proved by the Lyapunov stability theory.Finally,a simulation experiment is carried out with the two-joint manipulator as the research object.The results show that the fixed-time sliding mode tracking control strategy of the RBF neural network can estimate the uncertain parameters in the model,effectively improve the control effect,and make the controller have fixed-time convergence characteristics,which improves the convergence speed of the manipulator.

关 键 词：机械臂 径向基函数神经网络控制 固定时间滑模面 LYAPUNOV函数 收敛速度 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP273[自动化与计算机技术—控制科学与工程] TP241