Robust adaptive control with lumped model uncertainty and wind disturbance estimation for airship trajectory tracking

Area of Science:

• Robotics
• Control Systems Engineering
• Aerospace Engineering

Background:

• Robotic airships offer versatile platforms for communication, delivery, and data gathering.
• Achieving full autonomy in airships necessitates precise trajectory tracking control.
• Complex, uncertain nonlinear dynamics of airships present significant control challenges.

Purpose of the Study:

• To address the trajectory tracking control problem for robotic airships facing model uncertainties and wind disturbances.
• To develop a robust adaptive control solution for enhanced airship autonomy.
• To improve the stability, convergence, and reduce chattering in Sliding Mode Control (SMC).

Main Methods:

• Proposed a lumped model uncertainties and wind disturbance estimation using an Unscented Kalman Filter (UKF).
• Integrated the UKF-based estimation with a Sliding Mode Controller (SMC) to create the Unscented Kalman filter-based Sliding Mode Controller (USMC).
• Investigated the stability and convergence properties using Lyapunov stability analysis.

Main Results:

• The proposed USMC algorithm demonstrated efficient and precise trajectory tracking for the robotic airship.
• The method successfully estimated lumped model uncertainties and wind disturbances.
• Simulation results validated the effectiveness of the USMC in achieving desired trajectory tracking.

Conclusions:

• The USMC provides a robust adaptive control solution for robotic airship trajectory tracking.
• The method overcomes limitations of traditional SMC, including stability, convergence, and chattering issues, without requiring prior bounds on uncertainties.
• This advancement contributes to the realization of fully autonomous airship applications.