An unmanned Aerial Vehicle (UAV) is a highly non-linear unstable system. In this work using fractional order calculus, a novel fractional order dynamics of UAV is proposed. The concept of fractional order depicts the more realistic behavior of UAVs. For proposed fractional order model, a fractional order sliding mode controller (SMC) is designed such that the desired path can be achieved by the UAV in finite-time. In addition to this an adaptive neural network (ANN) based approximation function is attached to the controller having the quality of optimal hidden nodes. The weight associated to the hidden nodes achieves the optimal values. The integration of ANN based function with fractional order SMC achieves better results compared to fractional order SMC alone. Stability analysis is given for the fractional order SMC using fractional Lyapunov method. The same Lyapunov function has been used for finding the adaptive law for estimating the unknown dynamics of the system. Simulations have been done for position and attitude tracking of UAV using ANN based fractional order SMC to demonstrate the advantage of the proposed method.
Adaptive Neural Network Based Fractional Order Control of Unmanned Aerial Vehicle
Lect. Notes Electrical Eng.
International Conference on Informatics in Control, Automation and Robotics ; 2021 ; Paris, France July 06, 2021 - July 08, 2021
2023-02-25
17 pages
Article/Chapter (Book)
Electronic Resource
English
Neural network based feedback linearization control of an unmanned aerial vehicle
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