Controller design by symbolic regression
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- @Article{DANAI:2021:MSSP,
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author = "Kourosh Danai and William G. {La Cava}",
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title = "Controller design by symbolic regression",
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journal = "Mechanical Systems and Signal Processing",
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volume = "151",
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pages = "107348",
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year = "2021",
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ISSN = "0888-3270",
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DOI = "doi:10.1016/j.ymssp.2020.107348",
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URL = "https://www.sciencedirect.com/science/article/pii/S0888327020307342",
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keywords = "genetic algorithms, genetic programming, Symbolic
Regression, Nonlinear Control, Structural Adaptation,
Controller Design",
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abstract = "A novel method of empirical controller design is
introduced with the potential to produce exotic
controller forms. The controllers in this method are
derived by symbolic regression (SR) to be in equation
form, hence, they are legible in how the controller
output is computed as a function of loop variables.
Because SR is computationally costly due to its
extensive search of controller space, requiring
evaluation of millions, if not billions, of candidate
controllers, the candidate controllers cannot be
evaluated in closed-loop due to the high cost of
simulation associated with such evaluation. This paper
offers a recourse to this closed-loop evaluation by
allowing evaluations to be performed algebraically. To
this end, a method of inverse solution is introduced
that estimates the plant input for a desired plant
output. This estimated plant input is then used as the
target output for candidate controllers that can be
readily evaluated algebraically based on the available
time series of loop variables associated with the
desired plant output. Unlike traditional control design
which relies on closed-loop performance metrics to
provide controller performance guarantees, the proposed
open-loop approach sacrifices such guarantees in favor
of new controller forms that it may yield. Therefore,
the fidelity, as controllers, of candidate controllers
need to be verified post-design. For this purpose, the
candidate controllers are first evaluated as
controllers in closed-loop simulation. Once verified by
simulation, they need to be validated for closed-loop
stability, as demonstrated for one of the studied
cases",
- }
Genetic Programming entries for
Kourosh Danai
William La Cava
Citations