Discrete-Time Adaptive Neural Tracking Control and Its Experiments for Quadrotor Unmanned Aerial Vehicle Systems

Xiuyu Zhang, Yue Wang, Guoqiang Zhu, Xinkai Chen, Chun Yi Su

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


For the control of a quadrotor unmanned aerial vehicle, the strong nonlinearities, coupling, and underactuated problem in both positioning and attitude systems of the quadrotor are major challenging issues to be solved. In this article, a discrete-time adaptive dynamic surface control (DSC) scheme for the quadrotor is proposed to obtain a satisfactory tracking performance. The nonlinearities and couplings are overcome by employing the designed robust adaptive DSC nonlinear control method. The underactuated problem is overcome by solving the designed adaptive neural control equations. Also, different from the continuous-time control scheme, the discrete-time control is more suitable for the computer and network control in practicable cases. Furthermore, the digital first-order low-pass filters are constructed to predict the future virtual control signal in the backstepping method, leading to the avoidance of the model transformation problem in the discrete-time backstepping method.

Original languageEnglish
JournalIEEE/ASME Transactions on Mechatronics
Publication statusAccepted/In press - 2021


  • Aerodynamics
  • Attitude control
  • Backstepping
  • Discrete time
  • Mathematical models
  • Propellers
  • Rotors
  • Vehicle dynamics
  • dynamic surface control (DSC)
  • quadrotor
  • robust adaptive control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Discrete-Time Adaptive Neural Tracking Control and Its Experiments for Quadrotor Unmanned Aerial Vehicle Systems'. Together they form a unique fingerprint.

Cite this