Neural Network-based Constrained Optimal Coordination for Heterogeneous Uncertain Nonlinear Multi-agent Systems

TL;DR

This paper presents a neural network-based distributed control approach for heterogeneous nonlinear multi-agent systems, ensuring convergence to constrained optimal solutions despite uncertainties and disturbances.

Contribution

It introduces a novel composite controller combining internal models and neural networks for constrained optimal coordination in uncertain multi-agent systems.

Findings

Agents' outputs reach the constrained optimal point

The controller handles unknown nonlinearities and disturbances

Effectiveness demonstrated through two examples

Abstract

In this paper, we investigate a constrained optimal coordination problem for a class of heterogeneous nonlinear multi-agent systems described by high-order dynamics subject to both unknown nonlinearities and external disturbances. Each agent has a private objective function and a steady-state constraint about its output. We develop a composite distributed controller for each agent by a combination of internal model and neural network. All agent outputs are proven to reach the constrained minimal point of the aggregate objective function with bounded residual errors irrespective of the unknown nonlinearities and external disturbances. Two examples are finally given to demonstrate the effectiveness of the algorithm.