Output-feedback online optimal control for a class of nonlinear systems
arXiv:1903.02078 · doi:10.23919/ACC.2019.8814910
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.