This paper deals with the robust exact pole placement problem in connection with the solvability of a Sylvester equation. The main issue is to compute a wellconditioned solution to this equation. The best candidate solution must possess the minimal condition number. This problem is cast as a global optimization under LMI constraints for which a numerical convergent algorithm is provided and compared with the most attractive methods in the literature.

In this paper, a constrained discete model predictive control (CDMPC) strategy for a greenhouse inside temperature is presented. To describe the dynamics of our system’s inside temperature, an experimental greenhouse prototype is engaged. For the mathematical modeling, a state space form which fits properly the acquired data of the greenhouse temperature dynamics is identified using the subspace system identification (N4sid) algorithm. The obtained model is used in order to develop the CDMPC starategy which role is to select the best control moves based on an optimization procedure under the constraints on the control notion. For efficient evaluation of the proposed control approach Matlab/Simulink and Yalmip optimization toolbox are used for algorithm and blocks implementation. The simulation results confirm the accuracy of the controller that garantees both the control and the reference tracking objectives.

This technical note deals with the robust exact pole placement problem: pole placement algorithms that guarantee a small variation of the assigned poles against possible perturbations. The solution to this problem is related to the solvability of a Sylvester-like equation. Thus, the main issue is to compute a well-conditioned solution to this equation. Also, the best candidate solution must possess the minimal condition number, to reduce sensitivity to perturbation. This problem is cast as a global optimization under linear matrix inequality constraints, for which a numerical convergent algorithm is provided and compared with the most attractive methods in the literature.Index Terms-Condition number, global optimization, linear matrix inequality (LMI), pole placement, Sylvester equation.

The present paper, introduces Adaptive Neuro Fuzzy Inference System (ANFIS) as one of the most mature and intelligent methods to predicte internal temperature and relative humidity of a greenhouse system. To conduct the application of the proposed strategy, an experimenntal greenhouse equipied with several sensors and actuators is engaged. In this sense a data base was collected during a period of day time where the temperature and relative humidity dynamics were observed inpresence of others climatic parameters and the actuators’ actions. The results demonstrate that by using ANFIS method, the predictions match the target points with a good accuracy. Therefore, the effectiveness of the strategy in term of both inside climate parameters’ prediction is guaranteed.

No abstract