Control of Unknown (Linear) Systems with Receding Horizon Learning
Systems and Control
2020-12-16 v3 Systems and Control
Optimization and Control
Abstract
A receding horizon learning scheme is proposed to transfer the state of a discrete-time dynamical control system to zero without the need of a system model. Global state convergence to zero is proved for the class of stabilizable and detectable linear time-invariant systems, assuming that only input and output data is available and an upper bound of the state dimension is known. The proposed scheme consists of a receding horizon control scheme and a proximity-based estimation scheme to estimate and control the closed-loop trajectory. Simulations are presented for linear and nonlinear systems.
Cite
@article{arxiv.2010.05891,
title = {Control of Unknown (Linear) Systems with Receding Horizon Learning},
author = {Christian Ebenbauer and Fabian Pfitz and Shuyou Yu},
journal= {arXiv preprint arXiv:2010.05891},
year = {2020}
}