M
Miguel Martínez
Researcher at Polytechnic University of Valencia
Publications - 83
Citations - 1616
Miguel Martínez is an academic researcher from Polytechnic University of Valencia. The author has contributed to research in topics: Multi-objective optimization & Model predictive control. The author has an hindex of 22, co-authored 82 publications receiving 1508 citations.
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A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization
TL;DR: A new graphical representation, called Level Diagrams, for n-dimensional Pareto front analysis is proposed, which consists of representing each objective and design parameter on separate diagrams and can be coloured in order to introduce designer preferences.
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Model-based predictive control of greenhouse climate for reducing energy and water consumption
TL;DR: In this paper, an alternative to classical climate control is proposed based on an accurate non-linear model and a model-based predictive control (MBPC) that incorporates energy and water consumption.
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Controller tuning using evolutionary multi-objective optimisation: Current trends and applications
TL;DR: In this paper, a design procedure based on evolutionary multi-objective optimisation (EMO) is presented and significant applications on controller tuning are discussed, but these statements are not commonly used in controller tuning.
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A new perspective on multiobjective optimization by enhanced normalized normal constraint method
TL;DR: In this paper, a new utopia hyperplane is proposed to improve the original normalized normal constraint method using two approaches: a redefinition of the anchor points and an exact linear transformation between the design objectives space and the normalized space.
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Nonlinear predictive control based on local model networks for air management in diesel engines
TL;DR: A detailed nonlinear engine simulator based on a first-principles model that provides an extensive range of possibilities in the experimentation field, because complex and innovative algorithms can be tested in a nondestructive way.