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

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A New Neural Dynamic Learning Framework for Discrete-Time Strict-Feedback Systems: Internal Interaction-Based Weight

Min Wang, Penghai Wen, Xiangpeng Xie

IEEE Transactions on Cybernetics

|August 8, 2023

Summary

This study introduces a novel learning control (LC) framework for uncertain systems. It enables neural network weights to converge to a single constant, improving storage and robustness.

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Area of Science:

Control Systems Engineering
Artificial Intelligence
Neural Networks

Background:

Uncertain discrete-time strict-feedback systems pose challenges for traditional control methods.
Existing learning control (LC) frameworks struggle with weight convergence in predictive models.

Purpose of the Study:

To develop an internal interaction-based dynamic learning control (LC) framework for uncertain discrete-time strict-feedback systems.
To address the issue of divergent neural weight convergence in predictive control models.

Main Methods:

The original system is transformed into an n-step-ahead predictive model.
The predictive model is decomposed into n one-step-ahead subsystems (agents).
Distributed cooperative weight adaptive laws are designed using an interconnection topology.

Main Results:

A novel internal weight interaction-based neural dynamic LC framework is proposed.
The framework ensures ultimate uniform boundedness and excellent control performance.
Estimated weights converge to a unique ideal constant, not multiple different constants.

Conclusions:

The developed LC framework enhances knowledge storage and utilization efficiency.
It improves the robustness of control systems with uncertain dynamics.
Simulation results validate the proposed framework's effectiveness and benefits.