Abstract
A new controller design method for the quadrotor helicopter based on the extreme learning machine (ELM) is proposed. ELM based neural controller and sliding mode controller are combined to stabilize the attitude systems of quadrotors including roll, pitch and yaw. A single hidden layer feedforward network based on ELM with fast learning speed is used to approximate the unmodeled nonlinear attitude dynamics while the sliding mode controller is employed to eliminate the external disturbances. In this way, precise dynamic model and prior information of disturbances are not needed. The simulation study is presented to show the effectiveness of the proposed control algorithm.
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Zhang, Y., Xu, B., Li, H. (2015). Adaptive Neural Control of a Quadrotor Helicopter with Extreme Learning Machine. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, KA. (eds) Proceedings of ELM-2014 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-14066-7_13
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DOI: https://doi.org/10.1007/978-3-319-14066-7_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14065-0
Online ISBN: 978-3-319-14066-7
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