Abstract
This paper describes the development of a closed-loop attitude estimation system for determining attitude reference for vehicle dynamics using fuzzy inference and Genetic Algorithms (GAs). By recognizing the situation of dynamic condition via fuzzy inference process, each parameter of the estimator of the attitude estimation system is determined online adaptively under varying vehicle dynamics. For this solution scheme, fuzzy rules and reasoning method are consider based on the error signal of the gyro and accelerometer and the magnitude of dynamic motion, and the input gains of the fuzzy systems and the position of the membership function are optimized based on the GAs. Computer simulations based on the real test data of a vehicle are used in the study to assess the system performance with the proposed fuzzy-GAs estimation method.
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Kim, MS. (2007). Performance Improvement of the Attitude Estimation System Using Fuzzy Inference and Genetic Algorithms. In: Melin, P., Castillo, O., RamÃrez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_45
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DOI: https://doi.org/10.1007/978-3-540-72432-2_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72431-5
Online ISBN: 978-3-540-72432-2
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