Nonlinear model predictive control of greenhouse temperature using a Volterra model

In mild climates, the greatest problem faced by far in greenhouse climate control is cooling, which, for economical reasons, leads to natural ventilation as a standard tool. The nonlinear relationship between ventilation and temperature can be captured by Volterra models. These models represent the simple and logical extension of convolution models and can be successfully applied in nonlinear model-based predictive control. This paper presents the development of a nonlinear model predictive controller (NMPC) based on the identification of a Volterra model from input/output data considering the natural ventilation and the most relevant disturbances acting on the system. Finally, the NMPC is applied to a detailed simulation model of the greenhouse and the control behavior will be illustrated by means of simulation results.