摘要
以农业机器人精密轨迹优化自动控制为目标,在优化算法中引入BP神经网络与计算力矩法结合的自动控制器,旨在减少作业过程中的运动误差,提高其工作效率。首先,建立农业机器人数学模型,分析其运动学和动力学原理;然后,设计了农业机器人运动控制系统,引入BP神经网络对不确定动力学因素进行判断,并提出解决该因素的自适应学习法;最后,对该系统运用Mat Lab进行了仿真。试验表明:以BP神经网络与计算力矩法结合的自动控制器可以有效优化机器人运动路径,提高机器人整体作业效率,系统运行稳定、可靠性强,且对外部环境的干扰因素具有较强的自适应学习能力。
In the trajectory optimization of precision agriculture robot, taking automatic control as the goal, it introduced the optimization algorithm combined with BP neural network and the computed torque method of automatic controller, which intended to reduce motion errors during the work and improve the work efficiency. In this paper, it first established mathematical model of agricultural robot, kinematics and dynamics analysis; then, it designed the agricultural robot mo- tion control system by using BP neural network to uncertain dynamics factors to judge, and put forward the solution to the factor of adaptive learning method. Finally the system used MATLAB simulation. Experimental result shows that the com- bined with BP neural network and the computed torque method of automatic controller, which can effectively optimize the robot motion path, and improve the overall operation efficiency of the robot, the system is stable and reliable, and the external environment interference factors with strong adaptive ability to learn.
出处
《农机化研究》
北大核心
2017年第6期33-37,共5页
Journal of Agricultural Mechanization Research
基金
河南省自然科学基金项目(2015GZC155)
南阳市科技攻关项目(KJGG36)
关键词
农业机器人
精密轨迹优化
BP神经网络
计算力矩法
agricultural robot
precision trajectory optimization
BP neural network
computational torque method
作者简介
袁铸（1982-），男，河南南阳人，讲师，硕士。
通讯作者：申一歌（1982-），女，河南南阳人，讲师，硕士研究生，（E-mail）yuanzhu1982@hnpi．cn。
