Nonlinear Adaptive Generalized Predictive Control for PH Model of Nutrient Solution in Plant Factory Based on ANFIS

Abstract

A plant factory is typically considered to be an exceedingly advanced product management system characterized by higher crop yields and better quality control. The pH value of the nutrient solution is crucial for determining the health and productivity of crops. However, the nutrient solution process exhibits inherent complexity, such as parameters uncertainty, multi-disturbances, and strong nonlinearity. Therefore, the traditional control method cannot meet the necessary requirements. The main objective of this paper is to address the issues of parameter uncertainty, strong nonlinearity, and multiple disturbances in the regulation process of the nutrient solution while achieving accurate control of the nutrient solution pH in a plant factory. This is performed so that a dynamic model of a nutrient solution for pH is developed and a nonlinear adaptive controller is presented, which comprises a linear adaptive generalized predictive controller, a nonlinear adaptive generalized predictive controller, and a switching mechanism. The parameters of the controller are adjusted by generalized predictive control (GPC) laws. In this approach, an adaptive neuro-fuzzy inference system (ANFIS) is used to estimate the unmodeled dynamics to depress the influence of nonlinearity on the system. The experiments show that the mean errors and standard errors for gain-scheduling the proportional-integral-derivative (PID) control strategy are 0.1388 and 0.4784, respectively. The mean errors and standard errors for the nonlinear adaptive controller are 0.1046 and 0.3009, respectively. Simulation results indicate that the presented method can acquire a better control effect in the case of various complex situations. Therefore, by achieving precise control of the pH value, it is possible to provide a suitable growth environment for crops, promoting healthy crop growth and increasing crop yield.