IOE OpenIR  > 光电技术研究所博硕士论文
基于时滞补偿的机动目标跟踪技术研究
曹政
学位类型硕士
导师毛耀
2017-05-23
学位授予单位中国科学院大学
学位授予地点北京
关键词机动目标 时滞补偿 预测跟踪 机器学习 Smith预估器
摘要

伴随着科学技术的飞速发展,目标的机动性也在不断地增强,尤其是小型无人机这类新型目标的出现,使得光电跟踪系统的跟踪能力已经很难满足目前的实际需求,需要进一步提高。而限制光电跟踪系统跟踪能力提升的因素有很多,其中图像传感器的时间滞后是一个主要因素,本文从时滞补偿的角度出发来提升系统的跟踪能力。

从控制结构来讲,经典控制包含的只有前馈和反馈,所有控制方法都是由这两种基本形式组成。本文从这两个方面出发对滞后进行补偿。利用前馈补偿滞后,需要通过状态估计方法来预测目标的速度或加速度,然后前馈到速度控制回路或加速度控制回路。利用反馈补偿滞后,则需要建立控制对象的预估模型,利用预估模型补偿掉含有滞后的反馈量。

为了能准确预测目标的速度或加速度,本文研究了预测滤波算法,对于小型无人机这类目标的跟踪,其难点在于目标运动模型的准确度。分析和研究了当前主流的交互式多模型算法,预测精度有较好的效果,但计算复杂,模型切换效率低,不适合实际使用。基于对交互式多模型算法的分析,提出了基于机器学习的预测前馈,通过机器学习的支持向量机对目标的运动状态进行分类,根据分类结果切换运动模型,滤波算法只需进行一次运算。仿真实验结果表明,提出的基于机器学习的预测前馈算法计算量远远小于交互式多模型算法,同时,保证了预测的精度,在模型切换效率上也优于交互式多模型算法。

此外,对于预估补偿本文重点研究了Smith预估控制,但传统的Smith预估控制对控制对象的参数变化很敏感,基于此,本文提出了与速度内回路相结合的方式,在传统Smith预估控制回路中增加一个内回路,相当于改善了控制对象的鲁棒性,并且从理论上分析了改进方案的有效性,在快反镜实验平台上设计了跟踪实验方案,实验结果表明,改进的Smith预估控制比传统的Smith预估控制更加稳定和有效。

其他摘要

With the fast development of science and technology, the maneuverability of the target is increasing, and the tracking ability of the electro-optical tracking system can’t meet the current actual requirements and is required to be further improved, especially, for the appearance of new targets such as the small unmanned air vehicle. However, there are many factors that restrict the improvement of tracking ability of the electro-optical tracking system, in which the time delay of image sensor is a major factor. In this paper, the tracking ability of the system is improved by the time delay compensation.

In terms of the control structure, the classical control only includes feed-forward and feedback, and all the control methods are composed by those two basic forms. Therefore, those two control structures are researched to compensate the time delay. By using feed-forward to compensate time delay, it is necessary to predict the velocity or acceleration of the target by estimation means, and then feed it to the velocity control loop or acceleration control loop. By using the feedback to compensate time delay, it is necessary to provide the model of the control plant, which can be used to compensate the delayed feedback signal.

In order to predict the velocity and acceleration of the target accurately, this paper has studied the predictive filtering algorithm. For the tracking of the small unmanned air vehicle, the difficulty is the accuracy of the target motion model. The interacting multi model algorithm has been analyzed and researched for the good prediction accuracy. However, the calculation of IMM is complex, and the model switching efficiency is low, therefore, it is not suitable for the actual application. Based on the analysis of the interacting multiple model algorithm, the predictive feed-forward based on machine learning is proposed, concretely, the motions of target are classified by a support vector machine which is kind of machine learning algorithm, and then, the motion model is switched according to the classification results, therefore, the filtering algorithm run only once in a period of iteration. Simulation results show that the amount of calculation of the proposed approach is far less than the interacting multiple model algorithm, and the accuracy of the prediction is ensured at the same time, in addition, the model switching efficiency is better than the interacting multi model algorithm.

Besides, this paper focuses on the research of Smith predictor control, but the traditional Smith predictor control is sensitive to plant parameter variations. Based on this, this paper puts forward the combination of velocity inner loop. A velocity inner loop is added in the traditional Smith predictor control loop, which results in the improvement of robustness of the controlled plant. The effectiveness of the proposed approach is analyzed in theory, and the experimental verification is designed in the fast steering mirror experiment platform. The experimental results show that the improved Smith predictor control is more stable and effective than the traditional predictor Smith control.

学科领域自动控制技术
语种中文
文献类型学位论文
条目标识符http://ir.ioe.ac.cn/handle/181551/8145
专题光电技术研究所博硕士论文
作者单位1.中国科学院光电技术研究所
2.中国科学院大学
推荐引用方式
GB/T 7714
曹政. 基于时滞补偿的机动目标跟踪技术研究[D]. 北京. 中国科学院大学,2017.
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