A Decision-Making Capability Optimization Scheme of Control Combination and PID Controller Parameters for Bivariate Fertilizer Applicator Improved by Using EDEM

Abstract

The fertilization rate is adjusted through the regulation of opening length and the rotational speed for bivariate fertilizer applicators. It is essential to optimally determine the control combination according to the target fertilization rate and further improve the control performance of fertilization operation in precision agriculture. In this study, a novel decision-making capability optimization scheme of control combination and PID controller parameters is proposed to improve the feasibility and practicability of variable fertilizer applicators. Firstly, EDEM is adopted to acquire the minimum allowable opening length and the proper gap between the spiral blades and the discharge cavity wall, and then calibration experiments are implemented to establish the fitting model of fertilization rate using polynomial fitting. Secondly, the modified sparrow search algorithm (SSA) with chaotic operator and mutation section of the DE algorithm is used to optimize the control combination utilizing the accuracy, uniformity, and adjustment time as the evaluation criteria. Moreover, the tent mapping bat algorithm (TBA) is applied to tune the PID controller parameters for enhancing the accuracy and response speed of the fertilization-rate control system. Compared to the PID controller based on the bat algorithm (BA), traditional PID controller, and fuzzy PID controller, the rise time of the PID controller improved by TBA decreases by 0.018 s, 0.09 s, and 0.038 s, respectively, and the average steady-state deviation of that drops by 0.02 kg ha−1, 1.45 kg ha−1, and 0.19 kg ha−1, respectively. In addition, under the condition of the same controller, compared with SSA, GA, and MOEA/D-DE, the average accuracy of the proposed decision-making algorithm decreases from 1.9%, 2.5%, and 3.5% to 1.8%, the average uniformity drops from 0.52% and 0.48% to 0.47%, and the average adjustment time declines from 0.99 s, 1.48 s, and 1.34 s to 0.5 s. It can be concluded that the method proposed in this study performs better in terms of accuracy and adjustment time but exhibits no apparent effect on the improvement of uniformity.