Study on Neural Network Self-Tuning PID Control for Temperature of Active Solar House Heating System

Aiming at temperature of active solar house heating system ,which has a non-linear, large time-delay, time-varying and model uncertain characteristics , to solve the problem, neural network self-tuning PID control algorithm is proposed. The algorithm takes the advantages of PID control algorithm for...

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Bibliographic Details
Published in2010 2nd International Workshop on Intelligent Systems and Applications pp. 1 - 4
Main Authors Tao Ai, Jun-qi Yu, Yan-feng Liu, Jiang Zhou
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2010
Subjects
Automatic control
Control systems
Neural networks
Noise robustness
Nonlinear control systems
Robust control
Solar heating
Temperature control
Three-term control
Time varying systems
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Summary:Aiming at temperature of active solar house heating system ,which has a non-linear, large time-delay, time-varying and model uncertain characteristics , to solve the problem, neural network self-tuning PID control algorithm is proposed. The algorithm takes the advantages of PID control algorithm for system adaptability and robustness, and has advantages of self-learning ability and adjusting the weights automatically in neural network for achieving perfect control effect on time-varying, noise disturbance characteristics of the parameters. Simulation results show that the algorithm has an effective suppression of non-linear, time-varying and time delay for the heating system, effectively improving the control accuracy and system adaptability for winning perfect control of the active solar house.
ISBN:1424458722
9781424458721
DOI:10.1109/IWISA.2010.5473685