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Event-/Self-Triggered Adaptive Optimal Consensus Control for Nonlinear Multiagent System With Unknown Dynamics and

Qinglai Wei, Hao Jiang

IEEE Transactions on Cybernetics

|March 3, 2025

Summary

This study introduces adaptive dynamic programming (ADP) for optimal consensus tracking in nonlinear multiagent systems (MASs) with unknown dynamics and disturbances. The method enhances control efficiency and resource conservation through event-triggered and self-triggered mechanisms.

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Area of Science:

Control Theory
Artificial Intelligence
Robotics

Background:

Nonlinear multiagent systems (MASs) present challenges in achieving consensus tracking due to unknown dynamics and external disturbances.
Existing control strategies often require continuous monitoring, leading to high computational and communication loads.

Purpose of the Study:

To develop an optimal consensus tracking control strategy for nonlinear MASs with unknown dynamics and disturbances.
To reformulate the control problem as a zero-sum differential game and solve it using adaptive dynamic programming (ADP).
To enhance control efficiency and resource management through event-triggered and self-triggered mechanisms.

Main Methods:

Adaptive dynamic programming (ADP) with a single neural network structure to approximate optimal control policies.
Reformulation of the problem as a multiplayer zero-sum differential game, treating disturbances as control inputs.
Implementation of event-triggering and self-triggering mechanisms to reduce controller workload and resource consumption.
Lyapunov stability analysis to prove system stability and boundedness of neural network weight estimation errors.

Main Results:

The proposed ADP-based control method effectively achieves optimal consensus tracking in nonlinear MASs.
Event-triggered and self-triggered mechanisms significantly reduce control system resource utilization.
Lyapunov stability analysis confirms the theoretical soundness and robustness of the control strategy.
Simulations on a single-link robot MAS validate the practical effectiveness of the proposed approach.

Conclusions:

The developed adaptive dynamic programming approach provides an effective solution for optimal consensus tracking in complex nonlinear multiagent systems.
The integration of event-triggered and self-triggered strategies offers a practical advantage in resource-constrained environments.
The study demonstrates the potential of ADP in addressing challenging control problems in autonomous systems.