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Abstract：
Plant factory is an important field of practice in smart agriculture which uses highly sophisticated equipment for precision regulation of the environment to ensure crop growth and development efficiently. Environmental factors, such as temperature and humidity, significantly impact crop production in a plant factory. Given the inherent complexities of dynamic models associated with plant factory environments, including strong coupling, strong nonlinearity and multi-disturbances, a nonlinear adaptive decoupling control approach utilizing a high-order neural network is proposed which consists of a linear decoupling controller, a nonlinear decoupling controller and a switching function. In this paper, the parameters of the controller depend on the generalized minimum variance control rate, and an adaptive algorithm is presented to deal with uncertainties in the system. In addition, a high-order neural network is utilized to estimate the unmolded nonlinear terms, consequently mitigating the impact of nonlinearity on the system. The simulation results show that the mean error and standard error of the traditional controller for temperature control are 0.3615 and 0.8425, respectively. In contrast, the proposed control strategy has made significant improvements in both indicators, with results of 0.1655 and 0.6665, respectively. For humidity control, the mean error and standard error of the traditional controller are 0.1475 and 0.441, respectively. In comparison, the proposed control strategy has greatly improved on both indicators, with results of 0.0221 and 0.1541, respectively. The above results indicate that even under complex conditions, the proposed control strategy is capable of enabling the system to quickly track set values and enhance control performance. Overall, precise temperature and humidity control in plant factories and smart agriculture can enhance production efficiency, product quality and resource utilization.
摘要：
植物工厂是智能农业的重要实践领域，它使用高度复杂的设备对环境进行精确调节，以确保作物有效生长和发育。环境因素,如温度和湿度，显著影响植物工厂的作物生产。鉴于与工厂环境相关的动态模型的固有复杂性，包括强耦合，强非线性和多干扰，提出了一种利用高阶神经网络的非线性自适应解耦控制方法，该方法由线性解耦控制器组成，非线性解耦控制器和开关功能。在本文中,控制器的参数取决于广义最小方差控制率，提出了一种处理系统不确定性的自适应算法。此外,利用高阶神经网络来估计未成型的非线性项，从而减轻非线性对系统的影响。仿真结果表明，传统控制器用于温度控制的平均误差和标准误差分别为0.3615和0.8425。相比之下,提出的控制策略在两个指标上都有了显著的改善，结果分别为0.1655和0.6665。对于湿度控制，传统控制器的平均误差和标准误差分别为0.1475和0.441。相比之下,提出的控制策略在两个指标上都有了很大的改善，结果分别为0.0221和0.1541。以上结果表明,即使在复杂条件下,所提出的控制策略能够使系统快速跟踪设定值并提高控制性能。总的来说,植物工厂和智能农业中的精确温度和湿度控制可以提高生产效率，产品质量和资源利用率。
