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Adaptive Neural Network Stochastic-Filter-Based Controller for Attitude Tracking With Disturbance Rejection.

Hashim A Hashim, Kyriakos G Vamvoudakis

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    Summary
    This summary is machine-generated.

    This study introduces a neural network (NN) stochastic filter-based controller for robust attitude tracking. The novel approach enhances control accuracy using low-cost sensors despite uncertainties and disturbances.

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    Area of Science:

    • Robotics and Control Systems
    • Aerospace Engineering
    • Applied Mathematics

    Background:

    • Attitude tracking is crucial for autonomous systems, but faces challenges from sensor noise and unknown disturbances.
    • Existing methods struggle with uncertainties inherent in low-cost inertial measurement units (IMUs).
    • Control on Lie groups, specifically the special orthogonal group SO(3), is essential for accurate attitude representation.

    Purpose of the Study:

    • To propose a novel, real-time neural network (NN) stochastic filter-based controller for attitude tracking.
    • To address measurement uncertainties, including bias and noise in angular velocity measurements.
    • To develop a robust control law capable of handling unknown disturbances.

    Main Methods:

    • An adaptive NN-based stochastic filter estimates attitude and dynamics from sensor measurements.
    • The filter design incorporates compensation for unknown bias and noise in angular velocity data.
    • A novel control law on the special orthogonal group SO(3) is developed, integrated with the NN filter.

    Main Results:

    • The proposed NN-based stochastic filter guarantees semiglobally uniformly ultimately bounded (SGUUB) closed-loop signals.
    • The integrated filter-based controller also achieves SGUUB closed-loop signals, ensuring stability.
    • Robust tracking performance is demonstrated using low-cost IMU data, even with high uncertainties and disturbances.

    Conclusions:

    • The NN stochastic filter-based controller provides a robust solution for attitude tracking problems.
    • The approach is effective under challenging conditions, including low sampling rates and high initialization errors.
    • Both continuous and discrete forms, including a unit-quaternion representation, are presented, enhancing applicability.