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WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

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Observer-based event-triggered control for zero-sum games of input constrained multi-player nonlinear systems.

Shunchao Zhang¹, Bo Zhao², Derong Liu¹

¹School of Automation, Guangdong University of Technology, Guangzhou 510006, China.

Neural Networks : the Official Journal of the International Neural Network Society

|September 3, 2021

Summary

This study introduces an event-triggered control (ETC) method using adaptive dynamic programming (ADP) for unknown multi-player nonlinear systems. The approach reduces computational load and saves resources by triggering updates only when necessary.

Keywords:

Adaptive dynamic programming Event-triggered control Input constraints Multi-player zero-sum games Neural networks

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Area of Science:

Control Systems Engineering
Nonlinear Dynamics
Artificial Intelligence

Background:

Zero-sum game (ZSG) problems in control systems are challenging, especially for unknown multi-player continuous-time nonlinear systems with input constraints.
Traditional control methods often require full knowledge of system dynamics and frequent communication, leading to high computational and resource burdens.

Purpose of the Study:

To develop an event-triggered control (ETC) method for solving ZSG problems in unknown multi-player nonlinear systems.
To reduce computational complexity and communication resource usage while ensuring system stability and performance.

Main Methods:

An adaptive dynamic programming (ADP) approach is employed, utilizing a neural network (NN) observer to identify system dynamics from input-output data.
A critic NN is constructed to solve the event-triggered Hamilton-Jacobi-Isaacs (HJI) equation, yielding approximated optimal control and disturbance laws.
A triggering scheme is designed to determine the optimal update instants for control and disturbance laws.

Main Results:

The proposed method effectively solves ZSG problems for unknown multi-player nonlinear systems with input constraints.
The event-triggered approach significantly reduces computational burden and conserves communication resources and bandwidth.
Stability of the closed-loop system and boundedness of NN weight approximation errors are proven using Lyapunov's direct method, with Zeno behavior excluded.

Conclusions:

The adaptive dynamic programming-based event-triggered control scheme offers an efficient and resource-saving solution for complex control problems.
The method demonstrates robustness and stability, validated through simulation examples.
This research contributes to advancing intelligent control strategies for uncertain, multi-player systems.