LQ evolution algorithm optimizer for model predictive control at model uncertainty

This paper presents an evolution algorithm as a powerful optimisation technique for tuning Model Based Predictive Control (MBPC) at the implications of different levels of model uncertainties. Although Standard Genetic Algorithms (SGAs) are proven to successfully tune and optimise MBPC parameters wh...

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Veröffentlicht in:2014 14th International Conference on Control, Automation and Systems (ICCAS 2014) S. 1272 - 1277
1. Verfasser: Osman, Haitham
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Institute of Control, Robotics and Systems (ICROS) 01.10.2014
Schlagworte:
Abstracts
Control Tuning
Genetic algorithms
Genetics
Model Base Predictive Control
Multi-objective Evolution Algorithm
Optical fibers
Tuning
ISSN:2093-7121
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Zusammenfassung:This paper presents an evolution algorithm as a powerful optimisation technique for tuning Model Based Predictive Control (MBPC) at the implications of different levels of model uncertainties. Although Standard Genetic Algorithms (SGAs) are proven to successfully tune and optimise MBPC parameters when no model mismatch. SGAs are trapped in a local optimum at the price of model uncertainty. The multi-objective evaluation algorithms are capable to incorporate many objective functions that can meet simultaneously robust control design objective functions. These promising techniques are successfully implemented to stabilised MBPC at high model uncertainty.
ISSN:2093-7121
DOI:10.1109/ICCAS.2014.6987752