@inproceedings{75303c1af58347e4b1ed4c0d4cdd3276,
title = "Adaptive Neural-Sliding Mode Control of a Quadrotor Vehicle with Uncertainties and Disturbances Compensation",
abstract = "This paper addresses the quadrotor vehicle control problem in the presence of parametric uncertainties and exogenous disturbances by introducing a finite-time extended disturbance observer-based adaptive neural sliding mode control (FTEDO-ANSMC) approach. The proposed FTEDO makes the controller robust to exogenous disturbances while eliminating the chattering issue in the control input. The designed SMC utilizes an adaptive neural network to tune its parameters online while a sliding mode concept-based weight update law is employed in the neural network to auto-update its weight parameters instead of conventional error-based weight update law without increasing the computational complexities, thereby enhancing the network's learning speed. The stability of the proposed control strategy is verified via Lyapunov theory. The simulation results of the proposed control strategy and its comparison with the conventional control strategy confirm its validity and efficacy.",
keywords = "Finite-time extended disturbance observer, Neural network, Quadrotor vehicle, Sliding mode control",
author = "Mati Ullah and Chunhui Zhao and Hamid Maqsood and Alam Nasir and Muhammad Humayun and {Ul Hassan}, Mahmood and Faiz Alam",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2nd IEEE International Conference on Artificial Intelligence, ICAI 2022 ; Conference date: 30-03-2022 Through 31-03-2022",
year = "2022",
doi = "10.1109/ICAI55435.2022.9773561",
language = "英语",
series = "2nd IEEE International Conference on Artificial Intelligence, ICAI 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "38--45",
booktitle = "2nd IEEE International Conference on Artificial Intelligence, ICAI 2022",
}