Hybrid Self-Learning Fuzzy PD + I Control of Unknown SISO Linear and Nonlinear Systems

Hdl Handle:
http://hdl.handle.net/11285/567125
Title:
Hybrid Self-Learning Fuzzy PD + I Control of Unknown SISO Linear and Nonlinear Systems
Authors:
Santana Blanco, Jesús
Issue Date:
2005-12-01
Abstract:
A human being is capable of learning how to control many complex systems without knowing the mathematical model behind such systems, so there must exist some way to imitate that behavior with a machine. In this dissertation a novel hybrid self-learning controller is proposed that is capable of learning how to control unknown linear and nonlinear processes incorporating human behavior characteristics shown when he/she is learning how to control an unknown process. The controller is comprised of a Fuzzy PD controller plus a conventional I controller and its corresponding gains are tuned using a human-like learning algorithm developed upon characteristics observed on actual human operators while they were learning how to control an unknown process reaching specified goals of steady-state error (SSE), settling time (Ts), and percentage of overshooting (PO). The systems tested were: first and second-order linear systems, the nonlinear pendulum, and the nonlinear equations of the approximate pendulum, Van der Pol, Rayleigh, and Damped Mathieu. Analysis and simulation results are presented for all the mentioned systems. More detailed results are provided for a nonlinear pendulum as a representative of nonlinear systems and for a second-order linear temperature control system as a representative of linear systems. This temperature system is used as a comparative benchmark with other controllers shown in the literature [10] that use this temperature control system, showing that the proposed controller is simpler and has superior results. Also, a robustness analysis is shown that demonstrates that the proposed controller keeps acceptable performance even under perturbation, noise, and parameter variations.
Keywords:
Hybrid Self-Learning; Fuzzy PD + I Control; Unknown SISO; Linear and Nonlinear Systems
Degree Program:
Doctor of Philosophy in Artificial Intelligence
Advisors:
Dr. Horacio Martínez Alfaro
Committee Member / Sinodal:
Dr. Manuel Valenzuela Rendón; Dr. Rogelio Soto Rodríguez; Dr. Eduardo Morales Manzanares
Degree Level:
Doctor of Philosophy in Artificial Intelligence
School:
Division of Information Technology and Electronics
Campus Program:
Campus Monterrey
Discipline:
Ingeniería y Ciencias Aplicadas / Engineering & Applied Sciences
Appears in Collections:
Ciencias Exactas

Full metadata record

DC FieldValue Language
dc.contributor.advisorDr. Horacio Martínez Alfaroes
dc.contributor.authorSantana Blanco, Jesúsen
dc.date.accessioned2015-08-17T09:29:48Zen
dc.date.available2015-08-17T09:29:48Zen
dc.date.issued2005-12-01en
dc.identifier.urihttp://hdl.handle.net/11285/567125en
dc.description.abstractA human being is capable of learning how to control many complex systems without knowing the mathematical model behind such systems, so there must exist some way to imitate that behavior with a machine. In this dissertation a novel hybrid self-learning controller is proposed that is capable of learning how to control unknown linear and nonlinear processes incorporating human behavior characteristics shown when he/she is learning how to control an unknown process. The controller is comprised of a Fuzzy PD controller plus a conventional I controller and its corresponding gains are tuned using a human-like learning algorithm developed upon characteristics observed on actual human operators while they were learning how to control an unknown process reaching specified goals of steady-state error (SSE), settling time (Ts), and percentage of overshooting (PO). The systems tested were: first and second-order linear systems, the nonlinear pendulum, and the nonlinear equations of the approximate pendulum, Van der Pol, Rayleigh, and Damped Mathieu. Analysis and simulation results are presented for all the mentioned systems. More detailed results are provided for a nonlinear pendulum as a representative of nonlinear systems and for a second-order linear temperature control system as a representative of linear systems. This temperature system is used as a comparative benchmark with other controllers shown in the literature [10] that use this temperature control system, showing that the proposed controller is simpler and has superior results. Also, a robustness analysis is shown that demonstrates that the proposed controller keeps acceptable performance even under perturbation, noise, and parameter variations.en
dc.language.isoen-
dc.rightsOpen Accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleHybrid Self-Learning Fuzzy PD + I Control of Unknown SISO Linear and Nonlinear Systemsen
dc.typeTesis de Doctoradoes
thesis.degree.grantorInstituto Tecnológico y de Estudios Superiores de Monterreyes
thesis.degree.levelDoctor of Philosophy in Artificial Intelligencees
dc.contributor.committeememberDr. Manuel Valenzuela Rendónes
dc.contributor.committeememberDr. Rogelio Soto Rodríguezes
dc.contributor.committeememberDr. Eduardo Morales Manzanareses
thesis.degree.disciplineDivision of Information Technology and Electronicses
thesis.degree.nameDoctor of Philosophy in Artificial Intelligencees
dc.subject.keywordHybrid Self-Learningen
dc.subject.keywordFuzzy PD + I Controlen
dc.subject.keywordUnknown SISOen
dc.subject.keywordLinear and Nonlinear Systemsen
thesis.degree.programCampus Monterreyes
dc.subject.disciplineIngeniería y Ciencias Aplicadas / Engineering & Applied Sciencesen
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