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This paper presents the implementation of a real-time adaptive controller to regulate the air temperature, humidity and CO2 concentration for a greenhouse located in the north of Portugal. The controller outputs are computed in real-time with the aim of achieving: adequate set-point tracking, minimization of the actuators efforts and minimization of the energy inputs for heating, cooling and CO2 injection. This is done by computing a sequence of future control actions to minimize a cost function over a prediction horizon of one hour. The cost function is proportional to the sum of the squared errors between the predicted and desired greenhouse climates plus the square of the energy inputs computed over the prediction horizon. The prediction climate models employ data from the air temperature, relative humidity and carbon dioxide concentration, inside and outside the greenhouse, solar radiation, wind speed and control actuating signals. In this way the performance of the adaptive controller depends largely on the accuracy of the prediction models. Since the greenhouse-crop system is time-variant, recursive identification techniques were applied to adapt in real-time the models parameters. Several simulations and experiments were realized with the proposed real-time adaptive controller and the results achieved proved to be suitable for this application. Also, the controller performance was compared with the one achieved using common PID, Proportional-Integrative-Derivative, controllers. With the proposed algorithm the set-point tracking and the minimization of actuator effort and energy consumption are significantly improved.