Experimental implementation of Gain scheduled Complementary filter and comparison with Kalman filter base on Low-Cost sensors

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:

Determination or state estimation is one of the pillars of the Attitude Control System. Sensors are the main instrument for determining the attitude of the system, but due to the presence of various noises and disturbances affecting the attitude sensors, appropriate filtering methods should be used. Cost and weight reduction in flight systems has been very important, so the use of microelectromechanical sensors, due to energy consumption, weight and low price, has received much attention. But noise is one of the drawbacks of microelectromechanical sensors that by combining data from different sensors using fusion algorithms, the data can be improved. In this research, the Gain scheduled complementary filter algorithm on a low-cost sensor and in laboratory conditions, based on a test platform with high dynamics and noise is implemented and compared with Kalman filter data and fixed reference. Finally, laboratory tests that the proposed algorithm works more accurately than the Kalman filter by applying noises in a static state as well as considering the oscillatory motion.

Language:
Persian
Published:
Journal of Mechanical Engineering, Volume:53 Issue: 4, 2024
Pages:
95 to 103
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