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郑州航空工业管理学院2. 中国科学院 长春光学精密机械与物理研究所3. 长春工业大学 电气与电子工程学院
收稿日期:2012-02-16,
修回日期:2012-04-16,
网络出版日期:2013-03-20,
纸质出版日期:2013-03-15
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王威立 郭劲 曹立华 陈娟. 基于神经网络ELM数据融合的共轴跟踪[J]. 光学精密工程, 2013,21(3): 751-758
WANG Wei-li GUO Jin CAO Li-hua CHEN Juan. On-axis tracking based on nerve net ELM data fusion[J]. Editorial Office of Optics and Precision Engineering, 2013,21(3): 751-758
王威立 郭劲 曹立华 陈娟. 基于神经网络ELM数据融合的共轴跟踪[J]. 光学精密工程, 2013,21(3): 751-758 DOI: 10.3788/OPE.20132103.0751.
WANG Wei-li GUO Jin CAO Li-hua CHEN Juan. On-axis tracking based on nerve net ELM data fusion[J]. Editorial Office of Optics and Precision Engineering, 2013,21(3): 751-758 DOI: 10.3788/OPE.20132103.0751.
为了在光电跟踪伺服系统中实现共轴跟踪,采用神经网络极限学习机(ELM)对光电跟踪系统设备的运动状态及脱靶量进行了学习、训练和融合,得到了目标的速度和加速度信息。通过算法优化减少了ELM系统大约50%的运算量,使运算周期约为3.5 ms,满足光电跟踪系统的实时性要求。仿真结果表明,当目标运动速度为50 /s、加速度为30()/s
2
时,预测的目标速度在峰值时的误差大约为3()/s。最后,通过跟踪光学动态靶标进行了共轴跟踪实验验证。结果显示,系统最大跟踪误差由速度、位置闭环时的11.35减小到0.88,随机误差由8.2减少到7.6。与其它控制方法相比,提出的方法具有更高的实时性和精确度,能有效提高系统的跟踪精度。
To realize the on-axis tracking in an electro-optical tracking servo system
a nerve net Extreme Learning Machine (ELM) was adopted to obtain the velocity and acceleration of the target motion through learning
training and fusing data. Through algorithm optimization
the amount of computing is reduced by about 50 percent in the ELM system
and the period of computing is 3.5 ms
so as to meet the real-time of electro-optical tracking system. The fused target information was filtered through the 6 step Butterworth filter. The simulation result verifies that the predicted target velocity error is about 3 () / s at the peak of velocity when the target velocity is 50 () / s and the target acceleration is 30 () / s
2
. Finally
the electro-optical tracking system was used to track an optical dynamic target equipment. When the system is in a double-loop and on-axis control
tracking results show that the maximal system tracking error has decreased from 11.35to 0.88
and the random error decreased from 8.2to 7.6. As compared with other control methods
the proposed method has better real time performance and higher accuracy
and improves the system tracking precision significantly.
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