Application of Nonlinear Adaptive PID Control in Temperature of Chinese Solar Greenhouses

Abstract

The system of the Chinese solar greenhouses (CSGs) is required to ensure suitable environment for crops growth. However, the greenhouse system is described as complex dynamics characteristics, such as multi-disturbance, parameter uncertainty, and strong nonlinearity. Actually, the conventional PID control method is difficult to deal with above problem. To address above problem, a dynamic model of CSG is developed based on the energy conservation laws and a nonlinear adaptive control scheme, combining RBF neural network with incremental PID controllers, is applied to the temperature control. In this approach, parameters of PID controller are determined by the generalized minimum variance laws, and the unmodelled dynamics is estimated by RBF neural network. The control strategy is combined with a linear adaptive PID controller, a neural network nonlinear adaptive PID controller and switching mechanism. The simulation results show that the adopted method can achieve excellent control performance, which meets the actual requirements.