Trajectory Tracking Control of Autonomous Quadrotor Helicopter Using Robust Neural Adaptive Backstepping Approach
Publication: Journal of Aerospace Engineering
Volume 31, Issue 2
Abstract
This paper presents the design and analysis of a robust neural adaptive backstepping control (RNABC) for an autonomous quadrotor helicopter perturbed by time-varying external disturbances. The work uses a backstepping method and a radial basis function neural network (RBFNN), which estimates perturbation. A gravitational search algorithm (GSA) is included to optimize the backstepping controller for a nominal dynamic model of a helicopter. Disturbances caused by external sources are estimated using the global estimation attribute of the RBFNN. To further improve the control design performance, a robust compensator is introduced to eliminate the approximation error produced by the neural approximator. Asymptotical stability of the closed loop control system is analytically proven via the Lyapunov theorem. The main advantage of the proposed methodology is that it requires no advance knowledge of the disturbances. A quadrotor helicopter is simulated to track trajectories. The effectiveness of the controller is also validated by realistic effects such as model uncertainty and measurement noise. The results show the efficiency and usefulness of the designed approach.
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Acknowledgments
This work is supported by Universiti Teknologi Malaysia under the Research University Grant (Q.J130000.2523.15H39).
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Information & Authors
Information
Published In
Journal of Aerospace Engineering
Volume 31 • Issue 2 • March 2018
Copyright
©2017 American Society of Civil Engineers.
History
Received: Jun 29, 2016
Accepted: Jul 11, 2017
Published online: Nov 27, 2017
Published in print: Mar 1, 2018
Discussion open until: Apr 27, 2018
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