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FPGA-Based Embedded System Architecture for Micro-Genetic Algorithms Applied to Parameters Optimization in Motion ControlJAEN-CUELLAR, A. Y. , MORALES-VELAZQUEZ, L. , ROMERO-TRONCOSO, R. , OSORNIO-RIOS, R. A.
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control design, genetic algorithms, field programmable gate arrays, microprocessors, servo systems
genetic(22), algorithm(17), optimization(12), systems(9), design(8), controller(8), control(8), applications(7), system(6), implementation(6)
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About this article
Date of Publication: 2015-02-28
Volume 15, Issue 1, Year 2015, On page(s): 23 - 32
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.01004
Web of Science Accession Number: 000352158600004
SCOPUS ID: 84924804553
Meta-heuristic techniques are powerful tools used to find an optimal solution for complex problems to which classical techniques find difficult to solve. The features among all the meta-heuristic techniques are the high amount of computational resources spent on their implementation and the computing effort generated on their execution. For this reason, many works have proposed their use on the base of software methodologies without achieving online or real-time performance. In the present work, two strategies that implement the Genetic Algorithms are presented by using the micro-population concept with the objective of reducing computational resources, increasing the heuristic search speed, and providing simplicity in its design. Both strategies are implemented in hardware architecture; the first, as a software strategy in a proprietary embedded processor, the second, as a hardware co-processor unit. In order to validate the proposed approaches, several tests to optimize a motion controller in a servo system are presented and compared with a classical tuning technique.
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 Micro-genetic algorithms for detecting and classifying electric power disturbances, Jaen-Cuellar, Arturo Yosimar, Morales-Velazquez, Luis, Romero-Troncoso, Rene de Jesus, Moriñigo-Sotelo, Daniel, Osornio-Rios, Roque Alfredo, Neural Computing and Applications, ISSN 0941-0643, Issue S1, Volume 28, 2017.
Digital Object Identifier: 10.1007/s00521-016-2355-z [CrossRef]
 Centroid Update Approach to K-Means Clustering, BORLEA, I.-D., PRECUP, R.-E., DRAGAN, F., BORLEA, A.-B., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 17, 2017.
Digital Object Identifier: 10.4316/AECE.2017.04001 [CrossRef] [Full text]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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