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适当的灌溉方法和灌溉方式对提高农作物的产量至关重要。根据农作物的生长周期,适时适量的给水加肥,并精确控制施加的水肥比例,能够很好的促进农作物对水肥的吸收,提高水肥的利用率。针对精确水肥控制系统非线性、时变性、滞后性等问题,提出一种基于BP神经网络预测的模糊PID算法。BP神经网络具有自学习和预测的能力,可以做到事前控制,解决控制系统的大时滞问题。仿真结果表明,该算法的响应速度、超调量及鲁棒性均优于传统的PID调节和模糊PID调节。最后,通过水肥浓度精量灌溉实验,验证了采用这种新型优化算法的水肥浓度精量控制机具有更加优越的控制效果,达到了优化的目的。
Appropriate methods of irrigation and irrigation are crucial to increasing crop yields. According to the crop growth cycle, timely and appropriate amount of water and fertilizer, and the precise control of the proportion of water and fertilizer applied, can well promote the absorption of water and fertilizer crops, improve water and fertilizer utilization. Aimed at the problems of nonlinear, time-varying and hysteresis of accurate water-fertilizer control system, a fuzzy PID algorithm based on BP neural network prediction is proposed. BP neural network has the ability of self-learning and prediction, can be done in advance control, to solve the large-time delay control system problems. Simulation results show that the proposed algorithm is superior to traditional PID and fuzzy PID in terms of response speed, overshoot and robustness. Finally, through the experiment of precision irrigation of water and fertilizer concentration, it is verified that the water and fertilizer concentration precision control machine adopting this new optimization algorithm has more superior control effect and achieved the optimization goal.