Distributed Dynamic Self-Triggered Impulsive Control for Consensus Networks: The Case of Impulse Gain With Normal...

Area of Science:

• Control Theory
• Systems Engineering
• Distributed Computing

Background:

• Nonlinear multiagent systems (MASs) require effective control strategies for coordinated behavior.
• Existing impulsive control methods often face limitations in impulse gain and sampling frequency, leading to conservativeness.
• Distributed control schemes aim to reduce communication overhead and improve scalability.

Purpose of the Study:

• To develop a novel distributed static and dynamic self-triggered impulsive control for nonlinear MASs.
• To integrate self-triggered control with impulsive control, incorporating normally distributed impulsive gains.
• To achieve consensus in MASs while reducing communication and sampling frequency.

Main Methods:

• A distributed self-triggered impulsive control scheme is proposed.
• Impulsive gains are designed to follow a normal distribution.
• Consensus criteria are derived for the nonlinear MASs.

Main Results:

• The proposed controller achieves consensus in nonlinear MASs.
• The use of normally distributed impulsive gains reduces conservativeness.
• The self-triggered approach minimizes communication and sampling frequency.

Conclusions:

• The developed distributed impulsive control method enhances the generality and applicability of MAS control.
• The novel approach effectively addresses limitations in existing literature regarding impulse gain and sampling frequency.
• Simulations validate the theoretical findings and demonstrate the controller's effectiveness.