Abstract
The difficulty of accurately modeling the aerodynamics of the heading angle of unmanned aerial vehicles is well known. Furthermore, it is difficult to control the heading angle of an unmanned aerial vehicle (UAV) with a model-based method under the influence of wind disturbances. To address this problem, a novel model-free control strategy is proposed. First, a self-organizing type-2 fuzzy brain emotional learning network model is used to estimate the PPD (pseudo partial derivative). Finally, the stability is guaranteed by designing the Lyapunov function. Meanwhile, the performance of the proposed method is further verified by high-fidelity semi-physical simulations.
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