# Approximate Nonlinear Regulation via Identification-Based Adaptive Internal Models

arXiv: https://arxiv.org/abs/1907.05050 · HTML: https://mlsanity.com/paper/1907.05050/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [Neural Internal Model Control: Learning a Robust Control Policy via Predictive Error Feedback](https://mlsanity.com/paper/2411.13079/) (2024)
- [Nonadaptive Output Regulation of Second-Order Nonlinear Uncertain Systems](https://mlsanity.com/paper/2505.21838/) (2025)

## Abstract (arXiv)

This paper concerns the problem of adaptive output regulation for multivariable nonlinear systems in normal form. We present a regulator employing an adaptive internal model of the exogenous signals based on the theory of nonlinear Luenberger observers. Adaptation is performed by means of discrete-time system identification schemes, in which every algorithm fulfilling some optimality and stability conditions can be used. Practical and approximate regulation results are given relating the prediction capabilities of the identified model to the asymptotic bound on the regulated variables, which become asymptotic whenever a "right" internal model exists in the identifier's model set. The proposed approach, moreover, does not require "high-gain" stabilization actions.