Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Seven state Kalman filtering for LEO microsatellite attitude determination
ISPRA'10 Proceedings of the 9th WSEAS international conference on Signal processing, robotics and automation
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The Genetic Algorithms (GAs) have become a popular optimization tool for many areas of research and topology optimization, an effective design tool for obtaining efficient attitude determination and control systems. The power of the genetic algorithm is that it starts with several initial conditions to avoid convergence towards a local minimum, which results in a direct impact on maximizing the fitness function (cost). The use of the genetic algorithm can give rise to a simplified controller, easy to implement and which has similar performance of the original controller. This paper presents the results on the performance of the sliding mode attitude controller for Nadir attitude pointing based on the artificial neural network adjusted by a genetic algorithm for an active gravity gradient stabilised microsatellite.