Design and Robustness Analysis of Intelligent Controllers for Commercial Greenhouse

Abstract

Abstract. In a commercial greenhouse, variables, such as temperature and humidity, should be controlled with minimal human intervention. A systematically designed climate control system can enhance the yield of commercial greenhouses. This study aims to formulate a nonlinear multivariable transfer function model of the greenhouse model using thermodynamic laws by taking into account the variables that affect the Greenhouse Climate Control System. To control its parameters, Mamdani model-based Fuzzy PID is designed which is compared with the performance of proportional-integral (PI) and proportional-integral-derivative (PID) controllers to achieve a smooth control action. The Fuzzy logic based PID provides robust control actions eliminating the need for conventional tuning methods. The robustness analysis is performed using values obtained from real-time implementation for the greenhouse model for Fuzzy based PID, PI and PID controllers by minimizing the Integral Absolute Error (IAE) and Integral Square Error (ISE). The greenhouse model has strong interactions between its parameters, which are removed by Relative Gain Array (RGA) analysis, thereby providing an effective control strategy for complex greenhouse production. Further, the stability analysis of non-linear greenhouse model is conducted with the help of the bode plot and Nyquist plot. Results show that good control performance can be achieved by tuning the gain parameters of controllers via step responses such as small overshoot, fast settling time, less rise time, and steady-state error. Also, smoother control action was obtained with Fuzzy based PID making the Greenhouse Climate Control System stable.

A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems

“…Models related to the horticultural industry and especially to agricultural greenhouses often require considerable additional programming efforts in order to make them easy enough to be used [19], since, in addition to nonlinearity, they present fluctuation of the variables of state, coupling between the different variables, fluctuations of the system over time, variation of meteorological parameters, and uncontrollable climatic disturbances. Together, these lead towards studying and developing an intelligent controller and regulation, and control and domain models of the climatic environment of the internal atmosphere of the greenhouse [7].…”

“…ese models must be related to the external influences of outdoor climatic conditions (such as solar radiation, outdoor air temperature, wind speed, etc.) and to the actions carried out (such as ventilation, cooling, heating, among others) [6,13,19,20].…”

“…e management of production in a greenhouse requires decisions on several timescales and also needs a rapid and frequent response so that the system adapts to the alternative climatic conditions, seeking greater efficiency in energy consumption and therefore reducing production costs [7,8]. is is how the control of indoor environments is one of the key factors that drive the commercial production of greenhouses [19].…”

Energy saving by LED lighting in greenhouses : A process-based modelling approach

“…For example, it can be seen that the models of Katzin et al (2020b) (Chapter 3) and of Righini et al (2020) were both extensions of and that the models described in Peŕez-González et al (2018) and in Lammari et al (2020) both originated with Boulard et al (1996). The models used by Mohamed & Hameed (2018), Gharghory (2020), and Subin et al (2020) all originated with the model of Albright et al (2001). It can also be seen that several early models (Albright et al, 2001;Boulard et al, 1996;De Zwart, 1996;Van Henten, 1994;Van Ooteghem, 2007) are still used as a basis for many recent studies.…”

“…The same was true for 4 out of 9 cases of model reuse. Cases of model reuse or extension by authors unrelated to the original publication are limited to quite simple models, as can be seen by their decomposition in Table 2.3: Xu et al (2018a) reused the model of Van Henten (2003), Mohamed & Hameed (2018) reused the model of Albright et al (2001), Gharghory (2020) and Subin et al (2020) reused the model of Pasgianos et al (2003), andJomaa et al (2019) reused the model of Blasco et al (2007), while using parameters from a previous study. Model extension without shared coauthors is also reserved for relatively simple models, e.g.…”