Adaptive Asymptotic Neural Network Control of Nonlinear Systems With Unknown Actuator Quantization

Area of Science:

• Control Theory
• Nonlinear Systems
• Neural Networks

Background:

• Nonlinear systems often face challenges with actuator quantization, which can degrade performance.
• Existing control methods may struggle with unknown quantization nonlinearities.
• Adaptive control offers a promising avenue for handling system uncertainties.

Purpose of the Study:

• To develop an adaptive neural network control algorithm for nonlinear systems with unknown actuator quantization.
• To ensure all signals in the closed-loop system are ultimately bounded using Lyapunov theory.
• To achieve asymptotic tracking error through a novel adaptive law incorporating Barbalat's lemma.

Main Methods:

• Exploiting the sector property of quantization nonlinearity to analyze upper bounds.
• Employing a dynamic loop gain function-based approach to manage quantization effects.
• Utilizing a neural network-based adaptive control scheme with online parameter estimation.

Main Results:

• The proposed adaptive control algorithm guarantees ultimate boundedness of all closed-loop system signals.
• Asymptotic tracking error is achieved, demonstrating effective control under quantization.
• The adaptive law requires estimation of only one parameter for neural network weight updates.

Conclusions:

• The developed adaptive neural network control is effective for nonlinear systems with unknown actuator quantization.
• The method provides robust stability guarantees and precise tracking performance.
• This approach offers a significant advancement in adaptive control for systems with quantized actuators.