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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
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Neural-Network-Based Fully Distributed Adaptive Consensus for a Class of Uncertain Multiagent Systems
Summary
This study introduces a fully distributed neuroadaptive consensus controller for uncertain multiagent systems (MASs). The method ensures robust stability and convergence despite nonlinearities and disturbances, without needing leader information.
Area of Science:
- Control Systems Engineering
- Artificial Intelligence
- Networked Systems
Background:
- Multiagent systems (MASs) face challenges with unmodeled nonlinearities and unknown disturbances in achieving consensus.
- Existing distributed consensus methods often require global information or leader bounds, limiting their applicability.
Purpose of the Study:
- To develop a robust, fully distributed neuroadaptive consensus controller for uncertain MASs.
- To address leaderless, leader-follower, and multi-leader containment consensus problems.
Main Methods:
- Construction of robust consensus controllers with linear, discontinuous, and neural network terms.
- Dynamic weight update laws for neural network components.
- Theoretical analysis using graph theory, nonsmooth analysis, and Barbalat's lemma.
Main Results:
- Asymptotic convergence of consensus errors is theoretically proven.
- The proposed method is fully distributed, requiring no Laplacian eigenvalues or leader input bounds.
- Demonstrated effectiveness for leaderless, leader-follower, and multi-leader containment scenarios.
Conclusions:
- The novel neuroadaptive approach offers a powerful, distributed solution for consensus in complex MASs.
- This method enhances robustness and applicability by eliminating reliance on specific system parameters or leader information.
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