Greenhouse temperature and humidity method controlled by particle swarm BP neural network PID

Abstract

The invention discloses a greenhouse temperature and humidity method based on particle swarm BP neural network PID control. the method comprises the following steps: S1, carrying out the adjustment of a certain gradient on each PID control parameter according to the general principle of PID adjustment, and recording an expected value, a final value, a deviation and a corresponding PID control parameter after the temperature and humidity control is completed; S2, obtaining the expected value, the final value, the deviation and the PID control parameters are preprocessed, and training data of the control model after preprocessing; S3, determining and solving a fitness function of a particle swarm algorithm, and optimizing a weight and a threshold value of the neural network by using the particle swarm algorithm; S4, building a neural network control model by using a weight and a threshold value obtained by a particle swarm optimization result, and training the model by using the training data in the step S2; and S5, optimizing BP neural network PID control by using a particle swarm optimization algorithm to control the temperature and humidity of the greenhouse. The method can adapt to complex working conditions of the sunlight greenhouse, and the temperature and humidity of the sunlight greenhouse can be accurately controlled.

Description

technical field [0001] The invention belongs to the technical field of intelligent optimization control, and in particular relates to a temperature, room temperature and humidity method controlled by a particle swarm BP neural network PID. Background technique [0002] my country is the country with the largest greenhouse area in the world. How to improve the control method of the greenhouse so that the greenhouse can automatically regulate various environmental factors such as temperature, humidity and carbon dioxide in the greenhouse environment, so as to obtain a more suitable growth environment for plants is At present, the main direction and goal of my country's research and development in modern greenhouse environment control technology. [0003] In the existing greenhouse control technologies, most of the control technologies for temperature and humidity are conventional PID and BP-PID control. The greenhouse is a complex object with strong time variation, strong coup...

AI Technical Summary

Problems solved by technology

The greenhouse is a complex object with strong time variation, strong coupling, nonlinearity, and large parameter changes. Simply using PID control will lead to problems such as fluctuations in the internal environmental factors of the greenhouse and low control accuracy. Although BP-PID combines the BP neural network However, the BP neural network itself has disadvantages such as slow convergence speed, long operation time, and easy to fall into local optimum.

In order to solve the problems of low precision and long response time of traditional greenhouse control methods, it is obviously necessary to combine control theory and intelligent algorithms

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