Abstract
This paper presents an improved model reference adaptive controller (I-MRAC) with RBF neural network approximation to deal with the model uncertainties, unknown actuator dynamics and input saturation of the unmanned aerial vehicle (UAV). On the one hand, the output of the RBF neural network (NN) is used as the compensator to eliminate the uncertainties of the system. On the other hand, the reference model is modified to deal with the unknown actuator dynamics and input saturation, improve stability and robustness, and prevent the high frequency oscillations. Meanwhile, the stability of the whole closed-loop system is proved by the Lyapunov analysis. The numerical simulation results of UAV attitude control demonstrate the effectiveness of the proposed method.
J. Zhang—Research supported by National Nature Science Foundation under Grant 61603220; Shandong Key Re-search and Development Program Grant 2019GGX103049; SDUST Young Teachers Teaching Talent Training Plan under Grant BJRC20190504.
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Gai, W., Liu, Y., Zhang, J., Zhang, G. (2022). Improved Model Reference Adaptive Controller with RBF Neural Network Approximation for UAV. In: Wu, M., Niu, Y., Gu, M., Cheng, J. (eds) Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021). ICAUS 2021. Lecture Notes in Electrical Engineering, vol 861. Springer, Singapore. https://doi.org/10.1007/978-981-16-9492-9_176
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DOI: https://doi.org/10.1007/978-981-16-9492-9_176
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