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传统的光电跟踪伺服系统滑模控制中,对目标位移信号微分以求取速度、加速度等运动状态的方法会影响控制的精度和稳定性。针对这一缺点,提出了基于改进容积卡尔曼滤波(CKF)的二阶滑模控制算法。基于系统原理,建立了系统模型;应用限定下界法改进了CKF算法以提高目标状态估计的准确度;应用超螺旋算法设计了二阶滑模控制器,并将滤波预测的目标状态作为系统输入进行仿真实验。结果表明,相对于传统的二阶滑模控制,所设计的控制方法提高了控制精度,减小了抖振,且具有更好的稳定性。
In the traditional sliding mode control of electro-optical tracking servo system, the method of differentiating the target displacement signal to obtain the motion state such as velocity and acceleration will affect the accuracy and stability of control. In view of this shortcoming, a second order sliding mode control algorithm based on improved volumetric Kalman filter (CKF) is proposed. Based on the principle of the system, a system model is established. The CKF algorithm is improved by using the defined lower bound method to improve the accuracy of the target state estimation. The second-order sliding mode controller is designed by using super-helix algorithm, and the target state of filtering prediction is input as system input Simulation. The results show that compared with the traditional second-order sliding mode control, the designed control method improves the control accuracy, reduces the chattering and has better stability.