温室温度控制系统神经网络PID控制算法研究

在以优质、高效、高产为目的的现代化农业发展新阶段,温室自动化技术的研究受到广泛重视.对于温室自动控制系统,由于其非线性、强耦合、纯滞后、大惯性的自身特性,传统PID控制已难以满足高品质温室控制系统的需求.由于BP神经网络具有强大的学习能力及非线性映射性,将BP神经网络控制引入常规PID控制中,采用BP神经网络PID控制方案,设计温室温度的自动控制系统并进行仿真验证.仿真结果表明,相比于传统的PID控制系统,所设计的基于BP神经网络PID控制系统具有更强的自适应能力与稳健性,控制品质具有明显优势.

Study of a Control Algorithm for Greenhouse Temperature System Based on Neural Network of PID

The automation technology has been widely used in greenhouse for the purpose of new stage of modern agricultural development is high-quality,efficiency and high yield.For greenhouse automatic control system,due to its nonlinear,strong coupling,large inertia,pure lag,traditional PID control is difficult to meet the high quality of greenhouse control system requirements.This paper introduced the BP neural network control into conventional PID control,for BP neural network has strong ability of learning and nonlinear mapping,designing greenhouse temperature automatic control system and simulation,use the BP neural network PID control scheme.The simulation results show that,compared to the traditional PID control system,the design of PID control system based on BP neural network has better adaptive ability and robustness,control quality has obvious advantages.