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当前位置: 首页 > 文章 > 基于改进樽海鞘群算法的四旋翼飞行器姿态优化控制 农业机械学报 2019 (1) 243-250
Position: Home > Articles > 基于改进樽海鞘群算法的四旋翼飞行器姿态优化控制 Transactions of the Chinese Society for Agricultural Machinery 2019 (1) 243-250

基于改进樽海鞘群算法的四旋翼飞行器姿态优化控制

作  者：
丁力;高振奇;虞青
单  位：
江苏理工学院机械工程学院
关键词：
四旋翼飞行器;姿态控制;樽海鞘群算法;非奇异终端滑模;线性扩张状态观测器
摘  要：
针对集总干扰下四旋翼飞行器的姿态控制问题,提出了一种基于线性扩张状态观测器的快速连续非奇异终端滑模控制策略.该方法通过线性扩张状态观测器来估计和补偿集总干扰的影响,提高控制器的稳定性;借助非奇异终端滑模面有限时间收敛的特性设计控制律,加快控制器的收敛速度;控制器的稳定性分析由Lyapunov函数得以证明.引入樽海鞘群算法来优化控制器中的参数,提升控制性能.进一步地,引入一维正态云模型与自适应算子来克服参数整定算法的固有缺陷,增强其优化能力.通过仿真与试验验证了本文方法的有效性与实用性,结果表明:在阵风干扰下,本文方法比线性自抗扰控制策略具有更高的跟踪精度、更强的抗干扰能力与更快的响应速度.
作  者：
SHEN Mingxia;LU Pengyu;LIU Longshen;SUN Yuwen;XU Yi;QIN Fuliang;College of Engineering,Nanjing Agricultural University;New Hope Liuhe Co.,Ltd.;
关键词：
broiler;;body temperature;;infrared thermography;;convolutional neural network;;multiple linear regression;;BP neural network
摘  要：
In broiler production,the temperature under the wing is an important indicator of animal health and welfare condition. Body temperature detection method of broiler based on infrared thermography was proposed to achieve measurement of broiler body temperature accurately and rapidly.The detected region of interest( ROI) model of broiler head and leg,based on a convolutional neural network,was developed to extract the maximum temperature of its head and leg. Besides,combined with ambient temperature,humidity and light intensity,two different broiler wing temperature inversion models were proposed by multiple linear regression and back propagation( BP) neural networks,respectively.And the experimental results showed that,based on the deep convolutional neural network,the ROI detected model achieved a precision and recall rate of 96. 77% and 100% on the test dataset,respectively. What 's more,the temperature inversion models achieved an average relative error of0. 33% with multiple linear regression,while BP neural network was 0. 29%. Deep learning method was used to obtain the ROI temperature,which was superior to the image processing method,high in efficiency and high in generalization ability. BP neural network model error was less than the error of multiple linear regression network model. Therefore,BP neural network can be applied as a temperature inversion model of broiler wings. BP neural network had the ability of self-learning and self-adaptation,and its generalization ability was strong. Applying it to the inversion of temperature under the wing can improve the accuracy and adaptability of the model. This model provided reliable technical support for real-time monitoring of broiler body temperature.

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