About Greenhouse Environment and Climate Control

The purview of this Working Group is to hold symposia that cover research in and the application of systems theory, in particular dynamical systems theory and control theory, to gain insight on the greenhouse system environment and its control. Thus, the focus is on modeling approaches, analysis methods, simulation techniques, optimization procedures and design approaches to the greenhouse system. This Working Group encompasses research and technology of all greenhouse crops (vegetables, cut-flowers, pot plants).

The following areas of interest are covered at the symposia:

• System modeling: the mechanistic, black-box approaches and also numerical methods of computational fluid dynamics (CFD),
• The physics of the greenhouse environment by the application of thermodynamics: balances of mass and energy, energy analysis,
• Artificial intelligence and soft computing modeling approaches such as neural networks, fuzzy systems, neuro-fuzzy systems and auto-regressive with exogenous input variables (ARX) models,
• Systems' analysis methods including: stability, uncertainty, sensitivity, observability, controllability and nonlinear analysis (phase-plane, bifurcation and chaos),
• Systems' optimization methods, both classical approaches of nonlinear programming but also artificial intelligence methods such as evolutionary algorithms: genetic algorithms, evolution strategies, evolutionary computation, differential evolution and genetic programming,
• Bio-inspired algorithms such as Ant Colony optimization, Artificial Bee Colony optimization, Particle Swarm optimization and other heuristic approaches,
• Other so called global optimization algorithms,
• Applications of Control Theory in order to modify the climate in greenhouses and other controlled agriculture systems such as plant factories, and screen houses,
• All classical control theory methods for analysis and design of control systems such as root-locus, proportional integral derivative (PID), frequency response and state space,
• Control approaches for nonlinear systems such as optimal control and predictive control,
• Artificial intelligence based control methods such as neural networks, fuzzy logic and neuro-fuzzy.

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