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畜牧养殖环境监测自主移动平台轨迹跟踪控制算法
引用本文:吕恩利,韦鉴峰,王昱,赵俊宏,王飞仁,刘妍华.畜牧养殖环境监测自主移动平台轨迹跟踪控制算法[J].农业工程学报,2018,34(13):86-94.
作者姓名:吕恩利  韦鉴峰  王昱  赵俊宏  王飞仁  刘妍华
作者单位:1.华南农业大学工程学院,广州 510642;2.华南农业大学南方农业机械与装备关键技术教育部重点实验室,广州 510642;3.华南农业大学工程基础教学与训练中心,广州 510642
基金项目:国家科技支撑计划课题(2015BAD18B0303);现代农业产业技术体系建设专项资金(CARS-33-13);国家自然科学基金项目(51108194)
摘    要:为实现畜牧养殖环境全方位监测,开发了可实现轨迹跟踪的自主移动监测平台。以具有非完整约束特性的自主移动平台为研究对象,研究其轨迹跟踪问题。在平台的结构基础上,通过建立其运动学模型及误差模型,提出基于Lyapunov函数和反推(Backstepping)时变状态反馈控制方法的轨迹跟踪算法,实现自主移动平台转向轮转角和行驶速度的控制。仿真和试验表明:所提出的控制算法能使该平台较好的收敛于期望轨迹,当跟踪期望轨迹稳定后,控制参数为(k1,k2,k3,k4)=(0.1,0.2,0.15,0.3)时,直线轨迹跟踪误差为xe=±7 mm,ye=±9.8 mm,θe=±4.2°,圆弧轨迹跟踪误差为xe=±6.2 mm,ye=±8.3 mm,θe=±5.8°,取得良好的跟踪精度。该研究可为畜牧养殖环境监测自主移动平台轨迹跟踪控制提供参考。

关 键 词:环境监测  算法  模型  自主移动平台  轨迹跟踪  运动学模型  Lyapunov函数  Beckstepping
收稿时间:2018/1/17 0:00:00
修稿时间:2018/5/10 0:00:00

Trajectory tracking algorithm of autonomous mobile platform for animal husbandry environment information monitoring
Lü Enli,Wei Jianfeng,Wang Yu,Zhao Junhong,Wang Feiren,Liu Yanhua.Trajectory tracking algorithm of autonomous mobile platform for animal husbandry environment information monitoring[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(13):86-94.
Authors:Lü Enli  Wei Jianfeng  Wang Yu  Zhao Junhong  Wang Feiren  Liu Yanhua
Institution:1.College of Engineering, South China Agricultural University, Guangzhou 510642, China;2.Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, China;3.Engineering Fundamental Teaching and Training Center, South China Agricultural University, Guangzhou 510642, China
Abstract:In order to achieve all-round monitoring of livestock environment, an autonomous mobile monitoring platform for trajectory tracking was developed. The trajectory tracking control problem of autonomous mobile platform was studied. According to the structure of the platform, the system structure and function of the autonomous mobile platform were introduced, including all kinds of sensors that the platform itself needs to perceive, and the measurement sensors needed for animal environment monitoring. And in the structure based autonomous mobile platform, combined with the autonomous mobile platform of four wheel model characteristics and kinematics principle of the simplified two DOF bicycle model, through the analysis of two degree of freedom model to obtain the kinematics model of the bicycle, so as to obtain the platform of steering wheel angle and moving between speed and wheelbase. According to the position and posture relationship between the current point and the desired point of the platform, the error model and the error differential model were obtained by using the geometric method, and it was proved that the platform would eventually converge to the desired trajectory as long as the platform speed and steering wheel angle were accurately controlled. In addition, according to the characteristics of the nonholonomic constraints of the platform, the choice of the expected point was determined by the arc tangent point method. Based on the platform error differential model, a time-varying state feedback control algorithm based on Lyapunov function and Backstepping was proposed, and the trajectory tracking control rate was obtained to achieve the control of steering wheel angle and speed of autonomous mobile platform. The rate was simulated on the platform of Matla2015a software, using the obtained control. The simulation results show that when the initial error is large, the rate control algorithm can make the platform converge to the desired trajectory, the error over time tends to zero. When choosing the platform the maximum speed (vmax,γmax)=(0.18 m/s,0.49 rad), maximum acceleration =(0.25 m/s2,0.5 rad/s), the tracking stability, in the control parameters (k1, k2, k3, k4)=(0.1,0.2,0.15,0.3), linear tracking error is xe=±7 mm, ye=±9.8 mm, θe=±4.2°, arc trajectory tracking error is xe=±6.2 mm, ye =±8.3 mm, θe=±5.8°. The results show that the proposed platform trajectory tracking algorithm can make the convergence of the autonomous mobile platform better on the desired trajectory, and achieved good control accuracy and stability of tracking. The study provides a reference for the overall monitoring of the track tracking control of the autonomous mobile platform in the animal environment.
Keywords:environmental monitoring  algorithms  models  autonomous mobile platform  trajectory tracking  kinematic model  lyapunov function  beckstepping
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