基于新型神经网络PID控制器的温室温度控制技术  

Temperature Control Technology of Greenhouse Based on a Novel Neural Network PID Controller

作　　者：付丽辉[1,2] 尹文庆[2] 

机构地区：[1]江苏省淮阴工学院电子与电气工程学院,江苏淮安223003 [2]南京农业大学工学院,南京210031

出　　处：《计算机测量与控制》2012年第9期2423-2425,2428,共4页Computer Measurement &Control

基　　金：国家自然科学基金(60977038);江苏省科技支撑项目(BE2009100);淮安市科技支撑计划农业项目(SN1161)

摘　　要：针对温室温度控制系统所存在的大惯性、非线性等问题,对基于重新参数化的B样条神经网络以及考虑到早熟现象的改进粒子群算法的B-BP-PSO-PID控制器进行研究;提出由PSO寻优找到最适合的β因子,得到适合权值搜索的最佳B样条基函数;提出由考虑早熟处理的改进粒子群算法取代传统BP后向传播算法来作为学习算法,有效克服传统算法易于陷入局部最优的缺点;仿真中以某温室20个不同时间的温度数据作为测试样本,结果表明,新型控制器的平均超调量为9.9%,平均响应时间为8.7s,可以实现对温室温度的最优化控制。In view of the problems of nonlinear and great inertia existing in the temperature control system of greenhouse, the B-BP PSO-PID controller based on re parameterization B-spline neural networks and improved PSO considering prematurity is studied. The controller can find the best suitable β factor by PSO, receive the best B-spline basic functions fit for searching weights, at the same time, substitute improved PSO considering prematurity for conventional backward-propagation （BP） algorithm which overcome the shortcomings of easily going into local optimum. With 20 test samples of temperature data at different time in a greenhouse, the experiment results indi cate the B BP-PSO-PID controller＇average overshoot is 9.9%, the mean response time is 8.7s, it can achieve the optimal control for the temperature of greenhouse.

关 键 词：温室 温度控制 B样条函数 重新参数化 粒子群算法 PID 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]