Neural Network-Based Adaptive Control for Uncertain Nonlinear Systems with Input Quantization

Document Type

Conference Proceeding

Publication Date

1-1-2025

Abstract

This paper addresses the asymptotic stabilization problem for a class of uncertain nonlinear systems with logarithmic quantization at the control input. Different from the existing results and approaches, a Lyapunov function candidate and a neural network-based adaptive control law are developed to adaptively estimate the unknown nonlinear functions and to asymptotically stabilize the uncertain nonlinear system, in which the control input also involves uncertain parameters, possibly in the nonlinear form. We demonstrate that asymptotic stabilization can be achieved under mild conditions, even when the adaptive estimates do not converge to the true system functions. A sufficient condition for asymptotic stabilizability is derived in terms of quantization density and multiplicative parameter error bounds at the control input. Notably, the proposed neural-network-based adaptive controller guarantees the H∞-norm strictly less than γ (for any γ>1) for the uncertain linearized closed-loop system, effectively suppressing bounded network reconstruction errors. Simulation results validate the method's effectiveness.

Publication Source (Journal or Book title)

Proceedings of the 4th Conference on Fully Actuated System Theory and Applications Fasta 2025

First Page

2031

Last Page

2036

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