Simplex Guided Extremum Seeking Control With Convergence Detection to Improve Global Performance

This paper presents a novel approach to improve the global performance of extremum seeking control (ESC). This algorithm employs perturbation-based ESC to find local extrema and a simplex-based algorithm to search for the global extremum. The two methods are employed in an iterative switching manner...

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Vydáno v:IEEE transactions on control systems technology Ročník 24; číslo 4; s. 1266 - 1278
Hlavní autoři: Zhang, Yinghua, Rotea, Mario, Gans, Nicholas
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.07.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithm design and analysis
Algorithms
Control systems
Convergence
Convergence detection
Criteria
extremum seeking control (ESC)
Frequency-domain analysis
global optimization
Linear programming
Mathematical analysis
nonlinear control
Optimization
Performance enhancement
Prediction algorithms
Switches
Switching
visual search algorithms
extremum seeking control (ESC)
nonlinear control
visual search algorithms
Convergence detection
global optimization
ISSN:1063-6536, 1558-0865
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Shrnutí:This paper presents a novel approach to improve the global performance of extremum seeking control (ESC). This algorithm employs perturbation-based ESC to find local extrema and a simplex-based algorithm to search for the global extremum. The two methods are employed in an iterative switching manner. We propose a novel frequency-domain criterion to detect when the state of a system under ESC has converged to a local optimum. Switching from ESC to the simplex method is triggered when this criterion is satisfied. We prove that this criterion will be met within finite time, under conditions on the ESC law. Simulations and experiments demonstrate and statistically prove that this algorithm outperforms ESC or simplex-based algorithms for optimizing objective functions with multiple extrema.
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ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2015.2478079