关键词: 水肥控制, PID控制, PSO优化, BP神经网络, MATLAB仿真

Abstract:  Aiming at the problems of traditional irrigation and fertilization methods that cannot meet crop growth requirements coupled with the serious waste of water and fertilizer resources, a water and fertilizer control system based on PSO and BP neural network to optimize the PID model was designed. By combining the water and fertilizer concentration information of the crop planting environment, the system used the PSO and BP neural network algorithm to optimize PID control parameters to solve the problems of nonlinear, timevarying, and hysteresis in the water and fertilizer irrigation process. The MATLAB/Simulink simulation test results showed that the PID control model optimized by PSO and BP neural network was 9.33% faster than the traditional PID control model, the adjustment time was shortened by 72.24%, and the overshoot was only 11.78% of the PID control. The optimization effect was good. The system test results showed that the system control was stable during the irrigation process, and the effect of precise control of water and fertilizer concentration was achieved to a certain extent, which had practical application value.

Key words: water and fertilizer control, PID control, PSO optimization, BP neural network, MATLAB simulation