This thesis is devoted toMultiobjective Optimization Design (MOOD) procedures
for controller tuning applications, by means of EvolutionaryMultiobjective
Optimization (EMO).With such purpose, developments on tools, procedures
and guidelines to facilitate this process have been realized.
This thesis is divided in four parts. The first part, namely Fundamentals,
is devoted on the one hand, to cover the theorical background required for
this Thesis; on the other hand, it provides a state of the art review on current
applications of MOOD for controller tuning.
The second part, Preliminary contributions on controller tuning, states early
contributions using the MOOD procedure for controller tuning, identifying
gaps on methodologies and tools used in this procedure. The contribution
within this part is to identify the gaps between the three fundamental steps of
theMOOD procedure: problemdefinition, search and decisionmaking. These
gaps are the basis for the developments presented in parts III and IV.
The third part, Contributions on MOOD tools, is devoted to improve the
tools used in Part II. Although applications on the scope of this thesis are related
to controller tuning, such improvements can also be used in other engineering
fields. The first contribution regards the decision making process,
where tools and guidelines for design concepts comparison in m-dimensional
Pareto fronts are stated. The second contribution focuses on amending the gap
between search process and decisionmaking. With this in mind, a mechanism
for preference inclusion within the evolutionary process is developed. With
this it is possible to calculate pertinent approximations of the Pareto front;
furthermore, it allows to deal efficiently with many-objective and constrained
optimization instances.
Finally, in the fourth part, Final contributions on controller tuning, a stochastic
sampling procedure for proportional-integral-derivative (PID) controllers
is proposed, to guarantee that (1) any sampled controller will stabilize the
closed loop and (2) any stabilizing controller could be sampled. Afterwards,
two control engineering benchmarks are solved using this sampling strategy,
the MOOD guidelines highlighted trough this Thesis for multivariable controller
tuning and the tools developed in Part III. / Reynoso Meza, G. (2014). Controller Tuning by Means of Evolutionary Multiobjective Optimization: a Holistic Multiobjective
Optimization Design Procedure [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/38248
| Identifer | oai:union.ndltd.org:upv.es/oai:riunet.upv.es:10251/38248 |
| Date | 23 June 2014 |
| Creators | Reynoso Meza, Gilberto |
| Contributors | Blasco Ferragud, Francesc Xavier, Sanchís Saez, Javier, Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica |
| Publisher | Editorial Universitat Politècnica de València |
| Source Sets | Universitat Politècnica de València |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis, info:eu-repo/semantics/publishedVersion |
| Source | Riunet |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, info:eu-repo/semantics/openAccess |
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