带输入饱和的双摆桥式起重机神经网络滑模控制  

Neural Network Sliding Mode Control for Double-pendulum Overhead Crane with Input Saturation

作　　者：刘佳辉 程文明[1] 谌庆荣 杜润[1] LIU Jiahui;CHENG Wenming;CHEN Qingrong;DU Run(School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031,China)

机构地区：[1]西南交通大学机械工程学院,四川成都610031

出　　处：《控制工程》2024年第4期687-694,共8页Control Engineering of China

基　　金：国家自然科学基金面上项目(51675450);四川省科技计划重点研发项目(2019YFG0300);轨道交通运维技术与装备四川省重点实验室开放课题(2019YW001,2020YW002)。

摘　　要：针对现代工业中输入饱和受限的双摆桥式起重机防摇摆控制问题,设计了一种基于神经网络的非奇异终端滑模控制器。首先,分析起重机的非线性动力学系统,并引入抗饱和模块将系统所需的控制力限制在驱动电机能提供的最大驱动力内;然后,采用部分状态信息反馈控制设计控制器,该控制器只需起重机小车位置、速度的反馈信息,无须实时测量吊重和摆角;之后,利用所提控制器跟踪经过规划的S形平滑函数,并用神经网络逼近起重机系统中复杂未知的非线性函数部分;最后,通过李雅普诺夫稳定性理论对系统状态的稳定性进行分析。仿真结果表明,所提控制器能在保证起重机小车准确定位的同时,有效抑制吊钩和重物的残余摆动,并且对外界干扰具有较强的鲁棒性。A non-singular terminal sliding mode controller based on neural network is designed to solve the problem of anti-sway control of the double-pendulum overhead crane with limited input saturation in modern industry.Firstly,the nonlinear dynamic system of the crane is analyzed,and the anti-saturation module is introduced to limit the control force required by the system to the maximum driving force provided by the driving motor.Secondly,the partial state information feedback control is used to design the controller,which only needs the feedback information of the position and speed of the crane trolley,and does not need to measure the lifting weight and swing angle in real time.Thirdly,the proposed controller is used to track the planned S-shape smooth function,and the neural network is used to approach the complex unknown nonlinear function in the crane system.Finally,Lyapunov stability theory is used to analyze the stability of the system state.The simulation results show that the proposed controller can effectively suppress residual swing of the hook and weight while ensuring accurate positioning of the crane trolley,and has strong robustness to external disturbance.

关 键 词：输入饱和 部分状态反馈控制 神经网络 双摆起重机 李雅普诺夫稳定性理论 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]