Armando Ramírez-Arias

Multiobjective hierarchical control architecture for greenhouse crop growth

The problem of determining the trajectories to control greenhouse crop growth has traditionally been solved by using constrained optimization or applying artificial intelligence techniques. The economic profit has been used as the main criterion in most research on optimization to obtain adequate climatic control setpoints for the crop growth. This paper addresses the problem of greenhouse crop growth through a hierarchical control architecture governed by a high-level multiobjective optimization approach, where the solution to this problem is to find reference trajectories for diurnal and nocturnal temperatures (climate-related setpoints) and electrical conductivity (fertirrigation-related setpoints). The objectives are to maximize profit, fruit quality, and water-use efficiency, these being currently fostered by international rules. Illustrative results selected from those obtained in an industrial greenhouse during the last eight years are shown and described.

Modeling and Control of Greenhouse Crop Growth

A discussion of challenges related to the modeling and control of greenhouse crop growth, this book presents state-of-the-art answers to those challenges. The authors model the subsystems involved in successful greenhouse control using different techniques and show how the models obtained can be exploited for simulation or control design; they suggest ideas for the development of physical and/or black-box models for this purpose.Strategies for the control of climate- and irrigation-related variables are brought forward. The uses of PID control and feedforward compensators, both widely used in commercial tools, are summarized. The benefits of advanced control techniquesevent-based, robust, and predictive control, for exampleare used to improve on the performance of those basic methods.A hierarchical control architecture is developed governed by a high-level multiobjective optimization approach rather than traditional constrained optimization and artificial intelligence techniques. Reference trajectories are found for diurnal and nocturnal temperatures (climate-related setpoints) and electrical conductivity (fertirrigation-related setpoints). The objectives are to maximize profit, fruit quality, and water-use efficiency, these being encouraged by current international rules. Illustrative practical results selected from those obtained in an industrial greenhouse during the last eight years are shown and described. The text of the book is complemented by the use of illustrations, tables and real examples which are helpful in understanding the material.Modeling and Control of Greenhouse Crop Growth will be of interest to industrial engineers, academic researchers and graduates from agricultural, chemical, and process-control backgrounds.

IMPROVING EFFICIENCY OF GREENHOUSE HEATING SYSTEMS USING MODEL PREDICTIVE CONTROL

Abstract This article presents a comparison of commercial and model based predictive control strategies aimed at optimizing efficiency of classical heating systems used in greenhouse temperature control. Two kind of heating systems are considered: aerial pipes with hot water and air-fan heaters. By using simple linearized models of the system around the predefined setpoints and a generalized predictive control strategy, the performance is improved without requiring modifications in the heating systems. The main strength of this paper lies in the fact that the MPC algorithm has been tested in a greenhouse.

Discrete-time nonlinear FIR models with integrated variables for greenhouse indoor temperature simulation

This paper shows how Nonlinear Finite Impulse Response (NFIR) models realized by artificial neural networks can be used for developing simulation models of the inside temperature of greenhouses. The proposed NFIR models use integrated variables to reduce the number of past values needed as inputs. Several NFIR models have been developed using past data following a systems identification methodology. All data have been obtained from a real greenhouse in Southern Spain dedicated to tomato crop. The NFIR models are later compared with a model based on first principles. The results obtained in the a posteriori application of the models to new real data show that the performance of the NFIR model with integrated variables compares well with that of a first principles model, although the generalization capabilities of the latter are superior.

Control Strategies of Greenhouse Climate for Vegetables Production

The most extended control in the greenhouse industry across the world is the classical Proportional-Integral-Derivative control (PID).