Adaptive NN Distributed Control for Time-Varying Networks of Nonlinear Agents With Antagonistic Interactions

Area of Science:

• Control Theory
• Artificial Intelligence
• Networked Systems

Background:

• Distributed control systems often face challenges with nonlinear dynamics and unknown parameters.
• Achieving consensus in multi-agent systems with time-varying and signed network topologies is a complex problem.
• Unknown control directions in agents can significantly hinder the design of effective control algorithms.

Purpose of the Study:

• To develop an adaptive neural network (NN) distributed control algorithm for high-order nonlinear agents.
• To address the challenges posed by nonidentical unknown control directions (UCDs) and signed time-varying topologies.
• To achieve bipartite consensus in multi-agent systems under specific network conditions.

Main Methods:

• An adaptive neural network (NN) distributed control strategy is proposed.
• Nussbaum-type functions are utilized to handle unknown control directions.
• A novel lemma on convergence properties for antagonistic time-varying interactions is established.
• The control algorithm is designed for signed time-varying topologies that are cut-balanced and uniformly structurally balanced.

Main Results:

• The proposed NN distributed control algorithm guarantees convergence for a group of nonlinear agents.
• Bipartite consensus is achieved for high-order nonlinear agents with nonidentical UCDs under uniformly quasi-strongly δ-connected signed graphs.
• Simulation examples validate the effectiveness of the developed control algorithms.

Conclusions:

• The adaptive NN distributed control algorithm effectively manages high-order nonlinear agents with UCDs.
• The algorithm ensures system convergence and bipartite consensus under specified signed time-varying network conditions.
• This research contributes a robust solution for complex multi-agent coordination problems.