摘要
农业温室温度控制过程中,温度的精准控制是一个非线性、滞后的问题,实验表明,现有的PID控制很难实现对农业温度的辨识控制,控制过程的准确率,收敛速度慢等问题。提出建立农业温度控制模型,通过采用LI-RBF神经网络辨识器对农业温度控制系统进行辨识,以及LI-RBF神经网络与PID控制相结合,构成LI-RBF-PID控制策略。通过系统跟踪辨识结果比较,以及LI-RBF-PID控制器控制参数在线自整定的农业温度控制曲线表明,该方法优于PID控制,实现了农业温室温度智能辨识控制。
Agricultural greenhouse temperature control process, the temperature control is a precision linearity and hysteresis problems. The existing PID control is difficult to achieve the identification of agriculture temperature control, the accuracy of the control process, slow convergence problem. The establishment of agricultural temperature control model, by using LI-RBF neural network identifier temperature control system for agriculture identification, LI-RBF neural network and PID control are combined to form LI-RBF-PID control strategy. Comparison of tracking through the system identification and LI-RBF-PID controller parameters online self-tuning temperature control curve agriculture show that the method is superior to PID control, achieving intelligent identification of agricultural greenhouse temperature control.
出处
《计算机工程与应用》
CSCD
2013年第21期240-244,共5页
Computer Engineering and Applications
基金
甘肃省自然科学基金(No.1208RJZA191)
