Numerical Discrete-Time Implementation of Continuous-Time Linear-Quadratic Model Predictive Control
Abstract
This study presents the design, discretization and implementation of the continuous-time linear-quadratic model predictive control (CT-LMPC). The control model of the CT-LMPC is parameterized as transfer functions with time delays, and they are separated into deterministic and stochastic parts for relevant control and filtering algorithms. We formulate time-delay, finite-horizon CT linear-quadratic optimal control problems (LQ-OCPs) for the CT-LMPC. By assuming piece-wise constant inputs and constraints, we present the numerical discretization of the proposed LQ-OCPs and show how to convert the discrete-time (DT) equivalent into a standard quadratic program. The performance of the CT-LMPC is compared with the conventional DT-LMPC algorithm. Our numerical experiments show that, under fixed tunning parameters, the CT-LMPC shows better closed-loop performance as the sampling time increases than the conventional DT-LMPC.
Cite
@article{arxiv.2407.18825,
title = {Numerical Discrete-Time Implementation of Continuous-Time Linear-Quadratic Model Predictive Control},
author = {Zhanhao Zhang and Anders Hilmar Damm Christensen and Steen Hørsholt and John Bagterp Jørgensen},
journal= {arXiv preprint arXiv:2407.18825},
year = {2025}
}
Comments
This paper is accepted by DYCOPS 2025