Optimal temperature and humidity control for autonomous control system based on PSO‐BP neural networks

In order to solve the problems of difficult control, poor stability, and low control precision in complex autonomous non‐linear systems, and some sensors have non‐linear errors in special environments. Based on the PSO (Particle Swarm Optimization) algorithm, an PSO‐BP‐PID (Particle Swarm Optimization Back Propagation neural network PID) control method and a sensor error compensation algorithm based on BP (Back Propagation) neural network are designed for optimal temperature and humidity control and sensor error compensation in the autonomous greenhouse system. The error between the average temperature value and the target value after steady state is 0.5°C, and the error between the average humidity value and the target value is 1% RH. The results show that the control method can effectively compensate the non‐linear error of the sensor and improve the performance of the control system in a complex environment, which is suitable for the stable and control of actuators in autonomous systems. The error of temperature and humidity sensor is compensated by BP neural network; PSO (Particle Swarm Optimization) was used to optimize the BP‐PID parameters of the automatic greenhouse system. The error of temperature and humidity sensor is compensated by BP neural network; Particle Swarm Optimization (PSO) was used to optimize the BP‐PID parameters of the automatic greenhouse system.