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

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 c...

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Published inIEEE transactions on vehicular technology Vol. 67; no. 6; pp. 4793 - 4805
Main Authors Zhang, Zhijun, Zheng, Lunan, Guo, Qi
Format Journal Article
LanguageEnglish
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
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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.
AbstractList 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.
Author Zheng, Lunan
Zhang, Zhijun
Guo, Qi
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Snippet To stably control the position and attitude angles of an unmanned aerial vehicle (UAV), a varying-parameter convergent neural dynamic (VP-CND) method is...
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SubjectTerms 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
Title A Varying-Parameter Convergent Neural Dynamic Controller of Multirotor UAVs for Tracking Time-Varying Tasks
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