Model predictive control of nonlinear dynamical systems based on genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Feng:2017:CCC,
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author = "Qi Feng and Haowei Lian and Jindong Zhu",
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booktitle = "2017 36th Chinese Control Conference (CCC)",
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title = "Model predictive control of nonlinear dynamical
systems based on genetic programming",
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year = "2017",
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pages = "4540--4545",
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month = "26-28 " # jul,
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address = "Dalin, China",
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size = "5 pages",
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keywords = "genetic algorithms, genetic programming, model
predictive control, unknown nonlinear systems, neural
network",
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DOI = "doi:10.23919/ChiCC.2017.8028072",
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size = "6 pages",
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abstract = "Model predictive control (MPC) requires an explicit
dynamic model to predict values of the output variable,
so the accuracy of the model significantly affects the
quality of control. Unfortunately, it's hard to obtain
the explicit expression of unknown nonlinear systems in
MPC applications. This paper describes the use of
genetic programming (GP) to generate an empirical
dynamic model of a process, and to improve the
performance in providing accuracy and suitability
support for MPC strategy. GP derives both a model
structure and its parameter values in such a way that
the process trajectory is predicted accurately.
Consequently, the performance of the MPC strategy is
expected to improve on the performance obtained models.
Experimental results show that the GP based predictive
controller can obtain satisfactory performance.",
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notes = "Also known as \cite{8028072}",
- }
Genetic Programming entries for
Qi Feng
Haowei Lian
Jindong Zhu
Citations