Model-Free Adaptive Formation-Containment Control for UAV-UGV Systems Under Fading Channels

Area of Science:

• Robotics and Control Systems
• Networked Autonomous Systems
• Communication Engineering

Background:

• Cooperative control of multi-agent systems like unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) is crucial for complex missions.
• Fading communication channels pose significant challenges to the stability and performance of distributed control systems.
• Existing control methods often require accurate system models, which are difficult to obtain for UAV-UGV systems.

Purpose of the Study:

• To develop a model-free adaptive formation-containment control strategy for heterogeneous UAV-UGV systems.
• To address the challenges posed by fading communication channels in cooperative control scenarios.
• To ensure that control errors converge to specified bounds within finite time.

Main Methods:

• Linearization of nonlinear UAV and UGV dynamics into data models.
• Application of finite-time prescribed performance techniques for leaders and followers.
• Development of a compensation-based model-free adaptive control (MFAC) algorithm.

Main Results:

• Theoretical analysis confirms that leader tracking errors and follower compensated errors meet prescribed performance constraints.
• Control errors converge to terminal prescribed performance regions within finite time.
• The proposed MFAC algorithm effectively mitigates the adverse effects of fading channels.

Conclusions:

• The model-free adaptive formation-containment control scheme is effective for UAV-UGV systems operating under fading channels.
• The finite-time prescribed performance ensures rapid and bounded error convergence.
• Simulation results validate the proposed control strategy's performance and robustness.