Abstract
This paper presents an approach to merge three elements that are usually not thought to be combined in one application: evolutionary computing running on reasonably priced microcontrollers (μC) for real-time fast control systems. A Multi Objective Genetic Algorithm (MOGA) is implemented on a 180MHz μC.A fourth element, a Neural Network (NN) for supporting the evaluation function by predicting the response of the controlled system, is also implemented. Computational performance and the influence of a variety of factors are discussed. The results open a whole new spectrum of applications with great potential to benefit from multivariable and multiobjective intelligent control methods in which the hybridization of different soft-computing techniques could be present. The main contribution of this paper is to prove that advanced soft-computing techniques are a feasible solution to be implemented on reasonably priced μC -based embedded platforms.
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Dendaluce, M., Valera, J.J., Gómez-Garay, V., Irigoyen, E., Larzabal, E. (2014). Microcontroller Implementation of a Multi Objective Genetic Algorithm for Real-Time Intelligent Control. In: Herrero, Á., et al. International Joint Conference SOCO’13-CISIS’13-ICEUTE’13. Advances in Intelligent Systems and Computing, vol 239. Springer, Cham. https://doi.org/10.1007/978-3-319-01854-6_8
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DOI: https://doi.org/10.1007/978-3-319-01854-6_8
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