Design of temperature and humidity decoupling control system in plant factory based on RBF neural network PID
[Purpose]To explore and solve the problems of nonlinearity and strong coupling of environmental variables in the control of temperature and humidity environment inside plant factories,and to realize the adaptive control of temperature and humidity environment in plant factories.[Method]The step response experimental method was used to establish the control model of the plant factory temperature and humidity system,and the feedforward compensation decoupling method was used to decouple the temperature and humidity inside the plant factory;the Beetle Antennae Search algorithm(BAS)was used to optimize the initial value of the PID controller(Proportion Integration Differentiation)of the Radial Basis Function(RBF)neural network,and the optimized RBF neural network was used to adjust the PID parameters to achieve the control of temperature and humidity.[Result]The decoupled control system using BAS optimized RBF neural network PID,compared with the single neuron PID decoupled control system,the temperature and humidity curves changed more smoothly,the overshoot amount was almost 0,and the system was basically free of oscillation,and the overshoot amount of the temperature and humidity was reduced by 12.5%and 5.5%,respectively,and the regulation time was shortened by 80%and 84%.[Conclusion]The decoupled controller with PID parameter tuning by RBF neural network optimized by BAS has smaller overshooting and shorter time for temperature and humidity to reach stabilization,which not only decouples the coupling of temperature and humidity of the internal environment of the plant factory,but also improves the regulation speed and accuracy of the system.
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