Air-ground coordinated unmanned swarm systems: A multitasking framework for control design.

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence
• Aerospace Engineering

Background:

• Coordinating heterogeneous unmanned systems (UAVs and UGV) presents complex challenges.
• Simultaneous tracking and collision avoidance tasks create conflicting control objectives.
• System uncertainties necessitate robust and adaptive control strategies.

Purpose of the Study:

• To develop an effective control design for an air-ground heterogeneous unmanned swarm system.
• To address four critical tasks: formation flying, ground proximity, ground vehicle proximity, and trajectory tracking.
• To manage seemingly contradictory control requirements within the swarm system.

Main Methods:

• Consolidation of multiple tasks into a single performance measure (χ-measure) using creative transformations.
• Design of an adaptive robust control scheme incorporating a robust control component and an online adaptation law.
• Mathematical formulation to guarantee boundedness performance of the χ-measure.

Main Results:

• The proposed adaptive robust control effectively manages combined tracking and avoidance missions for the swarm system.
• The controller demonstrates outstanding performance despite the presence of conflicting task requirements.
• Guaranteed boundedness performance of the χ-measure was achieved under system uncertainties.

Conclusions:

• The developed control strategy successfully coordinates heterogeneous unmanned swarms with conflicting objectives.
• Adaptive robust control offers a viable solution for complex unmanned system coordination.
• The approach provides a robust framework for achieving reliable performance in uncertain environments.