Abstract
To stably control the position and attitude angles of an unmanned aerial vehicle (UAV), a varying-parameter convergent neural dynamic (VP-CND) method is proposed and applied. First, the dynamic models of multirotor UAVs are presented. Second, to meet the requirements of high accuracy and real-time control, a VP-CND method is proposed based on an error function to derive the position and attitude angle controllers. The existing fixed-parameter CND methods (e.g., the triple-Zhang dynamics or the Zhang dynamics and gradient dynamics) and the corresponding controllers are presented, and their limitations are analyzed. The proposed VP-CND control method not only can track time-varying target values but also possesses super-exponential convergence performance. Third, simulation comparisons verify the effectiveness, stability, and fast convergence of the VP-CND controllers.
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