Generalized predictive control—Part I. The basic algorithm
Automatica (Journal of IFAC)
Generalized predictive control—Part II. Extensions and interpretations
Automatica (Journal of IFAC)
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Model Predictive Control in the Process Industry
Model Predictive Control in the Process Industry
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Solving multivariables and non-linear problems with constrains is usual when dealing with control problems. The classical way to solve this was through the decomposition into less complex problems: sub-problems with less variables and through the use of linear approximated models. These methodologies can present good results, but for some, only a suboptimal solution with a poor quality can be reached. The aim of this work is to combine Model Based Predictive Control (MBPC), a powerful control technique, with Genetic Algorithms, a powerful optimization technique. This combination can overcome limitations when approaching very complex problems in an integral way. This work extends this application to Multi Inputs Multi Outputs modeled with state space representation (a general way to include a wide range of non-linearities) and shows its application to Greenhouse Climate Control.