Neural-Network-Based Adaptive Finite-Time Output Feedback Control for Spacecraft Attitude Tracking

Lin Zhao, Jinpeng Yu, Xinkai Chen

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


This brief is concerned with neural network (NN)-based adaptive finite-time output feedback attitude tracking control for rigid spacecraft in the presence of actuator saturation, inertial uncertainty, and external disturbance. First, a neural state observer is designed to estimate the unknown state. Then, based on the estimated state, the adaptive neural finite-time command filtered backstepping (CFB) is applied to construct virtual control signal and controller with updating law. The finite-time command filter is given to avoid the computation complexity problem in traditional backstepping, and the compensation signals based on fractional power are constructed to remove filtering errors. Using Lyapunov stability theory, we show that the attitude tracking error (TE) can converge into the desired neighborhood of the origin in finite time and all the signals in the closed-loop system are bounded in finite time although input saturation exists. The numerical simulations are used to show the effectiveness of the given algorithm.

Original languageEnglish
JournalIEEE Transactions on Neural Networks and Learning Systems
Publication statusAccepted/In press - 2022


  • Adaptive neural control
  • Artificial neural networks
  • Attitude control
  • Backstepping
  • Observers
  • Output feedback
  • Space vehicles
  • Uncertainty
  • attitude tracking control
  • backstepping
  • finite-time convergence
  • output feedback.

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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