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Model-Free Adaptive Optimal Control for Unknown Nonlinear Multiplayer Nonzero-Sum Game.

Qinglai Wei, Liao Zhu, Ruizhuo Song

IEEE Transactions on Neural Networks and Learning Systems

|October 27, 2020

Summary

This summary is machine-generated.

This study introduces an online adaptive optimal control algorithm for multiplayer nonzero-sum games in unknown nonlinear systems. The method uses adaptive dynamic programming and neural networks to find optimal control strategies without needing system models.

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Area of Science:

Control Theory
Game Theory
Artificial Intelligence

Background:

Multiplayer nonzero-sum games (MP-NZSG) are prevalent in various fields.
Controlling unknown nonlinear discrete-time systems presents significant challenges.
Existing methods often require accurate system models, limiting their applicability.

Purpose of the Study:

To develop a model-free adaptive optimal control algorithm for MP-NZSG.
To address discrete-time unknown nonlinear systems.
To enable online learning of optimal control policies.

Main Methods:

A model-free coupled globalized dual-heuristic dynamic programming (GDHP) structure was designed.
An online adaptive learning algorithm was developed to solve the Hamilton-Jacobi equation.
Neural networks (critic and action networks) were employed to approximate value functions and policies, with online weight updates.

Main Results:

The developed algorithm effectively solves MP-NZSG for unknown nonlinear systems.
Uniformly ultimate boundedness of neural network approximation errors was proven using Lyapunov analysis.
Simulation results validated the effectiveness of the proposed adaptive control scheme.

Conclusions:

The proposed online adaptive optimal control algorithm provides a robust solution for MP-NZSG in complex systems.
The model-free approach relaxes the need for system identification, enhancing practical applicability.
The use of adaptive dynamic programming and neural networks offers a powerful framework for intelligent control.