Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 34, Issue 10, Pages 8116-8123Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2022.3144493
Keywords
Attitude control; Space vehicles; Uncertainty; Output feedback; Backstepping; Artificial neural networks; Observers; Adaptive neural control; attitude tracking control; backstepping; finite-time convergence; output feedback
Ask authors/readers for more resources
This paper presents a neural network-based adaptive finite-time output feedback attitude tracking control method, which can effectively address issues such as actuator saturation, inertial uncertainty, and external disturbance in spacecraft. By designing a neural state observer and applying adaptive neural finite-time command filtered backstepping control, finite-time attitude tracking and controller state updating can be achieved. Numerical simulations confirm the effectiveness of the proposed algorithm.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available