# Output-feedback online optimal control for a class of nonlinear systems

## TL;DR

This paper introduces an output-feedback reinforcement learning method for controlling a specific class of nonlinear systems, combining model knowledge with dynamic state estimation to improve control performance.

## Contribution

It presents a novel output-feedback MBRL approach that integrates a dynamic state estimator with exact model knowledge for second-order nonlinear systems.

## Key findings

- Simulation results confirm the effectiveness of the proposed method.
- The approach successfully stabilizes the nonlinear systems under study.

## Abstract

In this paper an output-feedback model-based reinforcement learning (MBRL) method for a class of second-order nonlinear systems is developed. The control technique uses exact model knowledge and integrates a dynamic state estimator within the model-based reinforcement learning framework to achieve output-feedback MBRL. Simulation results demonstrate the efficacy of the developed method.