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Feedforward control based on orthogonal least square for a charge-coupled device-based tracking loop

机译:基于正交最小正方形的基于电荷耦合器件的跟踪环路的前馈控制

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A prediction feedforward control based data fusion is proposed to enhance closed-loop performance in electro-optical tracking system. The aim of feedforward controller is to estimate the target trajectory and velocity by synthesizing line-of-sight error and angular position from the encoder. However, time delay existing in every data fusion algorithm will still affect the tracking performance. In this paper, an orthogonal least square (OLS) prediction method is proposed to improve the tracking precision. The transfer function of OLS in frequency domain is built to analyze the time-delay characteristics, which indicates a less time-delay and higher bandwidth than classical Kalman filters. In the experiments, 50 sampled data and second order polynomial are used in OLS to tracking unmanned aerial vehicle (UAV) by electro-optical tracking system. Some comparative experiments compared with Kalman filter show that the prediction feedforward control based on OLS has better performance for reducing the tracking error.
机译:提出了一种基于预测的基于馈电控制的数据融合,以提高电光跟踪系统中的闭环性能。馈电控制器的目的是通过合成来自编码器的视线误差和角度位置来估计目标轨迹和速度。但是,每个数据融合算法中存在的时间延迟仍将影响跟踪性能。本文提出了一种正交最小二乘(OLS)预测方法来提高跟踪精度。构建OLS在频域中OL的传递函数以分析时间延迟特性,该特征表示比古典卡尔曼滤波器更少的时延和更高的带宽。在实验中,在OLS中使用50个采样数据和二阶多项式来通过电光跟踪系统跟踪无人驾驶飞行器(UAV)。与卡尔曼滤波器相比的一些比较实验表明,基于OLS的预测前馈控制具有更好的性能来减少跟踪误差。

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