基于LQEKF的机载光电平台的模型预测控制
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1.中北大学机械工程学院 太原 030051; 2.中北大学先进制造技术山西省重点实验室 太原 030051

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TP273

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中北大学先进制造技术山西省重点实验室开发课题研究基金(XJZZ201905)项目资助


Model predictive control of airborne opto-electronic platform based on LQEKF
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1.School of Mechanical Engineering, North University of China,Taiyuan 030051, China; 2.Shanxi Key Laboratory of Advanced Manufacturing Technology, North University of China,Taiyuan 030051,China

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    摘要:

    为了提高机载光电平台对目标稳定跟踪控制性能,提出一种基于线性二次增强卡尔曼滤波器的机载光电平台模型预测控制算法。建立机载光电平台的动力学模型,在卡尔曼滤波状态估计的基础上,引入线性二次调节器增益减小估计状态的相位延迟,使状态估计值更为精确,利用估计的状态设计模型预测控制器,减小目标跟踪误差。跟踪目标仿真实验结果与卡尔曼滤波状态估计结果最大误差减小了58.14%,与扩展卡尔曼滤波状态估计最大误差减小了52.62%,表明本算法能够有效提高机载光电平台对目标的跟踪控制性能,实现了机载光电平台对目标的稳定跟踪控制。

    Abstract:

    In order to improve the stable tracking control of airborne Opto-electronic platform, a model predictive control algorithm based on linear quadratic enhanced Kalman filter is proposed. A dynamic model of airborne Opto-electronic platform is established. Base on the state Kalman filter, a linear quadratic regulator gain is introduced to reduce the phase delay of the estimated state, which makes the state estimation more accurate. The maximum error between the simulation results of tracking target and the state estimation results of Kalman filter is reduced by 58.14%, and the maximum error between the extended Kalman filter and the state estimation is reduced by 52.62%. The simulation results show that the algorithm can effectively improve the tracking and control performance of the airborne optoelectronic platform, and realize the stable tracking and control of the airborne optoelectronic platform.

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晁育平,王日俊,贾凯旋.基于LQEKF的机载光电平台的模型预测控制[J].电子测量技术,2023,46(12):84-91

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  • 在线发布日期: 2024-01-31
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