Constrained temperature and relative humidity predictive control:Agricultural greenhouse case of study  

作　　者：Hafsa Hamidane Samira EL Faiz Iliass Rkik Mohamed El Khayat Mohammed Guerbaoui Abdelali Ed-Dahhak Abdeslam Lachhab 

机构地区：[1]Computer Engineering and Intelligent Electrical Systems Laboratory,Modeling,Materials and Control of Systems Team,Higher School of Technology,Moulay Ismail University of Meknes,Morocco

出　　处：《Information Processing in Agriculture》2024年第3期409-420,共12页农业信息处理(英文)

摘　　要：The importance of Model Predictive Control(MPC)has significant applications in the agricultural industry,more specifically for greenhouse’s control tasks.However,the complexity of the greenhouse and its limited prior knowledge prevent an exact mathematical description of the system.Subspace methods provide a promising solution to this issue through their capacity to identify the system’s comportment using the fit between model output and observed data.In this paper,we introduce an application of Constrained Model Predictive Control(CMPC)for a greenhouse temperature and relative humidity.For this purpose,two Multi Input Single Output(MISO)systems,using Numerical Subspace State Space System Identification(N4SID)algorithm,are firstly suggested to identify the temperature and the relative humidity comportment to heating and ventilation actions.In this sense,linear state space models were adopted in order to evaluate the robustness of the control strategy.Once the system is identified,the MPC technique is applied for the temperature and the humidity regulation.Simulation results show that the regulation of the temperature and the relative humidity under constraints was guaranteed,both parameters respect the ranges 15℃≤T_(int)≤30℃and 50%≤H_(int)≤70%respectively.On the other hand,the control signals uf and uh applied to the fan and the heater,respect the hard constraints notion,the control signals for the fan and the heater did not exceed 0≤uf≤4.3 Volts and 0≤uh≤5 Volts,respectively,which proves the effectiveness of the MPC and the tracking tasks.Moreover,we show that with the proposed technique,using a new optimization toolbox,the computational complexity has been significantly reduced.The greenhouse in question is devoted to Schefflera Arboricola cultivation.

关 键 词：Constrained model predictive control GREENHOUSE MISO systems OPTIMIZATION Relative humidity TEMPERATURE 

分 类 号：S625[农业科学—园艺学] TP273[自动化与计算机技术—检测技术与自动化装置]