Evolving behaviors for bounded-flow tracking control of second-order dynamical systems
Created by W.Langdon from
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- @Article{PENALOZAMEJIA:2019:EAAI,
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author = "Ollin Penaloza-Mejia and Eddie Clemente and
Marlen Meza-Sanchez and Cynthia B. Perez",
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title = "Evolving behaviors for bounded-flow tracking control
of second-order dynamical systems",
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journal = "Engineering Applications of Artificial Intelligence",
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volume = "78",
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pages = "12--27",
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year = "2019",
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keywords = "genetic algorithms, genetic programming, Behaviors,
Second-order dynamical systems, Nonlinear tracking
control, Bounded flow variable",
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ISSN = "0952-1976",
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DOI = "doi:10.1016/j.engappai.2018.10.001",
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URL = "http://www.sciencedirect.com/science/article/pii/S0952197618302069",
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abstract = "A two-stage methodology for the development of
nonlinear analytical controllers for tracking control
in second-order dynamical systems subject to flow
variable constraints is proposed. It extends the
concepts of behavior-based control to describe the
system as the summation of its unforced, forced, and
learned behaviors. While the unforced behavior is
characterized by its analytical dynamical model, the
forced and learned behaviors are introduced in the
system by means of a Control-Theory-based controller
and an evolutionary learning process based in the
Genetic Programming paradigm. The integration of both
approaches in a unified framework allows the system to
exhibit a good tracking performance while keeping the
flow variable bounded to a desired value, parametrized
as a boundary interval. A set of 180993 learned
behaviors, which preserves asymptotic convergence to
the desired behavior while achieving a bounded flow
variable, were discovered by the evolutionary process.
Simulation results show the effectiveness of the found
nonlinear tracking controllers with the highest fitness
value, as well as the one with the lower structural
complexity. A performance comparison between numerical
simulations and real-time experiments for a mechatronic
prototype is also provided to illustrate the
feasibility of the proposed method in real-world
applications",
- }
Genetic Programming entries for
Ollin Penaloza-Mejia
Eddie Helbert Clemente Torres
Marlen Meza-Sanchez
Cynthia B Perez
Citations