Discrete Model-Based Greenhouse Environmental Control using the Branch & Bound Algorithm

Abstract In this paper we propose the application of the Branch-and-Bound search algorithm to discrete model-based predictive control of greenhouses. The temperature control strategy is a mixture of temperature integration and difference between day and night temperatures. The general approach is presented and strategies are proposed in order to achieve a faster coverage of the solutions search space with reduced probability of loosing the optimal solution. The control energy requirements depend largely on the cost function coefficients and the evolution of the external climate. Fixed coefficients do not fully exploit the external climate predicted evolution in order to reduce energy consumption. A simple method is proposed to adapt on-line the cost function coefficients in a way that reduces energy consumption without significantly affecting control accuracy. The methods are briefly described and a subset of experimental and simulation results are presented.

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