Zhang Wanjun, Tong Jingsheng, Zhang Feng, Zhang Wanliang, Zhang Jingxuan, Zhang Jingyi, Zhang Jingyan, Sun Honghong, Northwood Derek O, Waters Kristian E, Ma Hao
Gansu ZeDe Electronic Technology Co. Ltd., Tianshui, China.
Gansu Dingxi Technology Co. Ltd., Tianshui, China.
PLoS One. 2025 May 22;20(5):e0324448. doi: 10.1371/journal.pone.0324448. eCollection 2025.
To address the fixed-parameter limitations of traditional PID control (e.g., excessive overshoot, prolonged settling time, poor adaptability to nonlinearities) and the insufficient real-time adjustment capability of conventional fuzzy PID control, which relies on empirically predefined rule bases, this study proposes a self-correcting fuzzy PID control strategy for agricultural water-fertilizer integrated systems. Traditional PID control, due to its static parameters, suffers from reduced stability and error accumulation under dynamic variations (e.g., irrigation flow fluctuations, environmental disturbances) or nonlinear interactions (e.g., coupling effects of fertilizer concentration and pH). While conventional fuzzy PID control incorporates fuzzy reasoning, its offline-designed rule bases and membership functions lack online adaptive parameter correction, leading to degraded precision in complex operating conditions. To tackle challenges posed by uncertain variables (e.g., time-varying soil permeability) and nonlinear parameters resistant to precise mathematical modeling, this research integrates fuzzy logic with an online self-correcting mechanism, constructs a mathematical model for the integrated control system, designs real-time correction rules, and validates the model through simulations using Matlab/Simulink and a semi-physical PC platform. The results demonstrate that the self-correcting fuzzy PID control significantly optimizes key performance metrics: overshoot (reduced by 21.3%), settling time (shortened by 34.7%), and steady-rate error (decreased by 18.9%), outperforming both traditional PID and fuzzy PID methods in concentration and pH regulation. Its parameter self-adaptation capability effectively balances dynamic response and steady-state performance, resolving issues such as overshoot oscillation and lagging regulation in nonlinear dynamics. In practical applications, the system achieved an average plant height growth rate of 15.86%-21.73% and a 30.41% yield improvement compared to the control group, validating the enhanced synergistic control of water and fertilizer enabled by the variable universe fuzzy PID approach. This study provides a robust control solution with theoretical innovation and practical value for managing complex nonlinear systems in precision agriculture.
为解决传统PID控制的固定参数局限性（如超调过大、调节时间过长、对非线性的适应性差）以及传统模糊PID控制实时调节能力不足的问题（传统模糊PID控制依赖经验预定义的规则库），本研究提出了一种用于农业水肥一体化系统的自校正模糊PID控制策略。传统PID控制由于其参数固定，在动态变化（如灌溉流量波动、环境干扰）或非线性相互作用（如肥料浓度和pH值的耦合效应）下，稳定性降低且误差累积。虽然传统模糊PID控制纳入了模糊推理，但其离线设计的规则库和隶属函数缺乏在线自适应参数校正，导致在复杂运行条件下精度下降。为应对不确定变量（如时变土壤渗透率）和难以精确数学建模的非线性参数带来的挑战，本研究将模糊逻辑与在线自校正机制相结合，构建了集成控制系统的数学模型，设计了实时校正规则，并通过Matlab/Simulink仿真和半物理PC平台对模型进行了验证。结果表明，自校正模糊PID控制显著优化了关键性能指标：超调量（降低21.3%）、调节时间（缩短34.7%）和稳态误差率（降低18.9%），在浓度和pH值调节方面优于传统PID和模糊PID方法。其参数自适应能力有效平衡了动态响应和稳态性能，解决了非线性动态中的超调振荡和调节滞后等问题。在实际应用中，与对照组相比，该系统实现了平均株高增长率为15.86%-21.73%，产量提高了30.41%，验证了变论域模糊PID方法实现的水肥协同控制增强效果。本研究为精准农业中复杂非线性系统的管理提供了一种具有理论创新性和实用价值的鲁棒控制解决方案。
