Disturbance observer-based predictive formation control of mobile robots under switching topology via neurodynamic...

Area of Science:

• Robotics
• Control Systems Engineering
• Distributed Systems

Background:

• Mobile robot formations require robust control strategies to handle dynamic network topologies.
• Persistent unknown disturbances pose significant challenges to maintaining coordinated behavior.

Purpose of the Study:

• To develop a disturbance observer-based robust distributed model predictive control (MPC) framework for mobile robot formation control.
• To address challenges posed by switching topologies and unknown disturbances simultaneously.

Main Methods:

• A novel framework combining disturbance observers with distributed MPC.
• Decoupling formation tasks into path-following and coordination using an improved virtual structure.
• Employing sequential quadratic programming and a neurodynamic optimizer for computational efficiency.

Main Results:

• Rigorous theoretical analysis proving closed-loop stability and recursive feasibility.
• Demonstrated robustness against switching topologies and disturbance observer estimation errors.
• Validation through comparative simulations and outdoor experiments on uneven terrain.

Conclusions:

• The proposed control strategy effectively achieves robust formation coordination for mobile robots.
• The method ensures reliable real-time performance in practical, complex scenarios.