摘要
设计了一种基于PID神经网络(PIDNN)算法的经纬仪精密转位控制系统,通过DSP运算控制伺服电机,同时驱动望远镜与照准部旋转,以实现自动照准目标;由于经纬仪转位时,照准部与望远镜分别绕竖轴和横轴旋转,导致系统参数变化,模型时变,而传统的PIDNN对时变模型适应性不强,速度轨迹跟踪效果不够理想;针对此不足,文章在经纬仪竖轴转位速度控制中,将望远镜旋转角度作为一个新的PIDNN输入变量引入系统;据此建立了改进PIDNN前向和反向传播算法,并推导了经纬仪转位系统的传递函数;Mat-lab仿真结果表明:新输入量的引入,有效补偿了模型参数变化对控制效果的不利影响,增强了系统的稳定性,提高了轨迹跟踪精度,从而为实现高精度转位控制的设计奠定了基础。
This article designs a kind of theodolite high--accuracy rotating system based on PID neural network (PIDNN). In order to ai- ming target automatically, DSP is used to control servo motors to drive telescope and alidade rotating at the same time. When theodolite is ro- tating, the telescope and alidade rotates with horizontal axis and vertical axis accurately. This causes the system parameter and the model to change. While the adaptability of traditional PIDNN is not so strong for time varying model. The result of trajectory of planning speed is not ideal. Aiming at this shortage, this article brings the rotation angle of telescope as a new PIDNN input variable quantity to the system. The forward ~ reverse spread arithmetic is set accordingly, and the transfer function of theodolite rotating system has also been deduced. The Matlab simulation result indicated that: the introduction of the new importing variable has effectively compensated the adverse effects of changes in model parameters to control effect. The stability of system is strengthened and the accuracy of trajectory tracking be improved. It also set the foundation for the design of high--accuracy rotating control.
出处
《计算机测量与控制》
CSCD
北大核心
2012年第11期3042-3044,3047,共4页
Computer Measurement &Control