回转支承自适应负载的运动控制方法

摘要

建立回转支承运动系统的动力学模型,以传统阻抗控制为基础,分析由于动力学模型不确定造成的误差,用神经网络补偿这一误差,建立基于力矩型神经网络阻抗控制结构,并对控制系统进行仿真分析.结果表明:力矩型神经网络阻抗控制器具有良好的自适应性及鲁棒性,能实现回转支承系统的驱动力和位置的双重高精度控制,从而降低功率损耗,有效减少损伤的发生机率和减缓损伤发展速度,延长使用寿命.%A dynamics model of a slewing bearing motion control system was established.Based on impedance control method,the error caused by uncertainty of the dynamics model was analyzed.The error was compensated by using neural network algorithm.A torque-based neural network impedance controller was developed and simulation analysis was conducted.The results showed that the controller had good adaptability and robustness,and it could control both driving torque and position precisely.Therefore,power loss,probability of damage occurrence and damage development rate could be reduced,and the service life of the slewing bearing could be extended.