<p>To highlight the conceptual aspects related to the implementation of techniques optimal control in the form state, we present in this paper, the identification and control of the temperature and humidity of the air inside a greenhouse. Using respectively an online identification based on the recursive least squares with forgotten Factor method and the multivariable adaptive linear quadratic Gaussian approach which the advanced technique (LQG) is presented.  The design of this controller parameters is based on state models identified directly from measured greenhouse data. hence the performances of the controller developed are illustrated by different tests and simulations on identified models of a greenhouse. Discussions on the results obtained are then processed in the paper to show the effectiveness of the controller in terms of stability and optimization of the cost of control.</p>

Abstract-This paper presents a study of a multivariable Adaptive Generalized Predictive Controller and its application to control the thermal behaviour of an agricultural greenhouse, which is composed of a number of different elements (cover, internal air, plants, soil, actuators and sensors). The thermal model was obtained after the study of energy balances reacting the physical behavior of the greenhouse. For this reason, we opted to estimate the dynamic model of the greenhouse with algorithm based on recursive least squares (RLS) method. Simulation results are exposed to show the controller's performances in terms of response time, stability and the rejection of disturbances.

This paper concerns the identification of a greenhouse described in a multivariab le linear system with two inputs and two outputs (TITO). The method proposed is b ased on the least squares identification method, without b eing less efficient, presents an iterative calculation algorithm with a reduced computational cost. Moreover, its recursive character allows it to overcome, with a good initialization, slight variations of parameters, inevitab le in a real multivariab le process. A comparison with other method s recently proposed in the literature demonstrates the advantage of this method. Simulations ob tained will be exposed to showthe effectiveness and application of the method on multivariab le systems.

No abstract