Distributed Adaptive Consensus of Nonlinear Heterogeneous Agents With Delayed and Sampled Neighbor Measurements.

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Distributed Systems

Background:

• Consensus problems are crucial for coordinating multi-agent systems.
• Existing methods often require full state information or real-time measurements.
• High-order nonlinear heterogeneous agents present unique control challenges.

Purpose of the Study:

• To address the adaptive output consensus problem for high-order nonlinear heterogeneous agents.
• To develop a control strategy using only delayed, sampled neighbor output measurements.
• To ensure asymptotic consensus and uniform boundedness of all closed-loop variables.

Main Methods:

• Introduction of n-times differentiable auxiliary variables incorporating agent and neighbor outputs.
• Development of an adaptive distributed backstepping design procedure.
• Proof of boundedness and regulation of auxiliary variables to zero for consensus.

Main Results:

• Asymptotic consensus among all agent outputs is guaranteed.
• The proposed adaptive control law utilizes only delayed, sampled neighbor output measurements.
• Uniform boundedness of all closed-loop variables is ensured.

Conclusions:

• The presented adaptive distributed backstepping approach effectively solves the output consensus problem.
• The method's reliance on limited, delayed measurements offers practical advantages.
• Simulation results validate the theoretical findings and control effectiveness.