Robust control strategy for multi-UAVs system using MPC combined with Kalman-consensus filter and disturbance...

Area of Science:

• Robotics
• Control Systems Engineering
• Aerospace Engineering

Background:

• Formation flight stability is challenged by external disturbances and data communication uncertainties between unmanned aerial vehicles (UAVs).
• Existing control strategies may struggle with real-time adaptation to complex environments and unpredictable external factors.

Purpose of the Study:

• To develop a robust and efficient control strategy for multi-quadrotor formation flight.
• To enhance formation stability, disturbance rejection, and trajectory tracking accuracy in dynamic environments.

Main Methods:

• A multi-constrained model predictive control (MPC) strategy was integrated with a Kalman-consensus filter (KCF) and a fixed-time disturbance observer (FTDOB).
• KCF was employed for robust data fusion amidst noise and uncertainty.
• FTDOB was utilized for real-time estimation and compensation of external disturbances.
• An improved MPC (IMPC) was designed for enhanced computational efficiency and system stability.

Main Results:

• The integrated strategy demonstrated effective data fusion, improving formation robustness in complex scenarios.
• FTDOB successfully estimated and compensated for external disturbances within a fixed time.
• The IMPC ensured asymptotic stability while improving computational efficiency.
• Simulations confirmed superior disturbance rejection, noise suppression, and accurate trajectory tracking for the formation.

Conclusions:

• The proposed control strategy significantly enhances the stability and performance of multi-quadrotor formations.
• The combination of KCF, FTDOB, and IMPC offers a robust solution for UAV formation control challenges.
• The developed method is effective in complex environments with external disturbances and data uncertainties.