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An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

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Finite-Time Attitude Tracking Control for Spacecraft Using Terminal Sliding Mode and Chebyshev Neural Network.

An-Min Zou, K D Kumar, Zeng-Guang Hou

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|January 27, 2011

Summary

This study introduces a finite-time spacecraft attitude control using terminal sliding mode and Chebyshev neural networks (CNNs). The novel approach ensures stability and accurate tracking despite uncertainties and constraints.

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Area of Science:

Aerospace Engineering
Control Systems Theory
Artificial Intelligence

Background:

Spacecraft attitude control is crucial for mission success.
Traditional methods face challenges with singularities, uncertainties, and constraints.
Accurate attitude tracking requires robust and adaptive control strategies.

Purpose of the Study:

To develop a finite-time attitude tracking control scheme for spacecraft.
To address global attitude representation without singularities using quaternions.
To ensure finite-time stability under various uncertainties and constraints.

Main Methods:

Utilizing terminal sliding mode control combined with Chebyshev neural networks (CNNs).
Employing quaternion-based four-parameter representation for singularity-free attitude description.
Implementing a switching function for adaptive NN control and robust control.
Applying a Lyapunov-based approach to guarantee finite-time stability.

Main Results:

The proposed controller achieves finite-time attitude tracking.
The Chebyshev NN effectively approximates unknown dynamics and disturbances online.
The switching function ensures bounded NN output and robust performance.
Simulations demonstrate effectiveness against unknown inertia, disturbances, and input constraints.

Conclusions:

The developed finite-time control scheme offers a robust solution for spacecraft attitude tracking.
The integration of terminal sliding mode and CNNs provides enhanced performance and stability.
The method successfully handles complex scenarios including uncertainties and constraints.