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This paper describes a model predictive controller design to regulate the greenhouse micro-climate, where the controller outputs are computed to optimise the future behaviour of the greenhouse's environment, concerning the setpoint accuracy of the internal temperature and humidity described by Takagi-Sugeno (T-S) model. Modelling procedure is based on two steps. First, the identification of the antecedent part where local linear models are valid using the well-known fuzzy C-means clustering algorithm. Then, recursive least squares (RLS) algorithm is used for consequent part parameters adaptation. An adaptive T-S fuzzy model is considered within the control scheme for prediction of the future greenhouse behaviour. The main way of controlling the greenhouse micro-climate is to use heating and ventilation to regulate both internal temperature and humidity. The simulation results show that the proposed approach maintains successfully both temperature and humidity within the greenhouse around the desired set points in the presence of disturbances. The simulation results are compared between MPC controller based on T-S fuzzy model and MPC based on a single linear model.

Abstract-Biological processes are complex, nonlinear uncertain systems and the state variables are not all available for measurement. For that, fuzzy modeling and state estimations are powerful methods for the control and diagnosis of these kind of systems. In this paper, we present some results obtained from the design of fuzzy controller and fuzzy sliding mode observer for an anaerobic digestion system. Index Terms-Anaerobic digester, fuzzy controller, linear matrix inequalities, robust state estimation, T-S fuzzy modeling.