Construction of greenhouse environment temperature adaptive model based on parameter identification

Abstract Rich literature has reported that there exists a nonlinear and time varying association between natural ventilation and temperature. This paper focus on the model of greenhouse temperature and parameter states estimation online. Adaptive control allows self-tuning of control parameters in the face of changing dynamics. The parameter identification of the model can select a suitable mathematical model according to the input and output of the data, and compare the matching with the original system model to determine the model category. The research has identified the assumed parameters in the model using system identification toolbox. A total of 90 groups of data were processed and model identification operations were performed for 3 months, then the transfer function of the greenhouse model was obtained. Detailed information has been acquired about the relevant model reference adaptive control system, including the description of the system structure, which is beneficial to the sub-module design of the model. The research suggests that the determined model is beneficial to the design of the controller. Finally, the stability test is carried out to ensure the designed adaptive control system basically stable. The research work has brought about a discovery of the error converges over time. Moreover, the output of the controlled object model after adaptive adjustment is gradually approached to the output of the reference model, achieving the adaptive control of the system.