A Varying-Parameter Convergent Neural Dynamic Controller of Multirotor UAVs for Tracking Time-Varying Tasks

• Published in: IEEE transactions on vehicular technology Vol. 67; no. 6; pp. 4793 - 4805
• Main Authors: Zhang, Zhijun, Zheng, Lunan, Guo, Qi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aerodynamics; Attitude control; Attitudes; Computer simulation; Control stability; Controllers; Convergence; Dynamic models; Error functions; neural dynamic method; Parameters; position and attitude angles; Robustness; Task analysis; Tracking; tracking control; Unmanned aerial vehicle (UAV); Unmanned aerial vehicles; Vehicle dynamics
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2018.2802909

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.