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

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Published on: August 15, 2020

A Q-modification neuroadaptive control architecture for discrete-time systems.

Konstantin Y Volyanskyy¹, Wassim M Haddad

¹Georgia Institute of Technology, Atlanta, 30332-0150, USA. gtg891s@mail.gatech.edu

IEEE Transactions on Neural Networks

|August 17, 2010

Summary

This study introduces a discrete-time neuroadaptive control framework using Q-modification for uncertain nonlinear systems. The new method ensures stability and performance by minimizing errors in neural network weight updates.

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Area of Science:

Control Systems Engineering
Artificial Intelligence
Dynamical Systems Theory

Background:

Neuroadaptive control frameworks are crucial for managing complex nonlinear systems with uncertainties.
Existing frameworks primarily focus on continuous-time systems, limiting applications in discrete-time environments.
Q-modification architecture offers a promising approach for enhancing control system robustness.

Purpose of the Study:

To extend the neuroadaptive control framework with Q-modification to discrete-time nonlinear uncertain dynamical systems.
To develop discrete-time update laws incorporating auxiliary Q-modification terms.
To ensure the stability and performance of the neuroadaptive control system in discrete-time applications.

Main Methods:

Extension of the continuous-time neuroadaptive control framework to discrete-time systems.
Development of discrete-time update laws utilizing Q-modification terms based on estimated neural network weights.
Characterization of neural network weights using auxiliary equations defining affine hyperplanes.
Minimization of an error criterion involving the sum of squares of distances between update weights and affine hyperplanes.

Main Results:

Successful extension of the neuroadaptive control framework to discrete-time systems.
Introduction of auxiliary Q-modification terms in discrete-time update laws.
Demonstration that Q-modification terms minimize an error criterion related to affine hyperplanes.
Validation of the framework's ability to handle nonlinear uncertain dynamical systems in discrete time.

Conclusions:

The proposed discrete-time neuroadaptive control framework with Q-modification is effective for uncertain nonlinear systems.
The Q-modification terms play a critical role in ensuring stability and performance by minimizing weight estimation errors.
This work provides a valuable tool for advancing control strategies in discrete-time applications.