Suboptimality analysis of receding horizon quadratic control with unknown linear systems and its applications in learning-based control

arXiv:2301.07876v2 Announce Type: replace-cross
Abstract: In this work, we aim to analyze how the trade-off between the modeling error, the terminal value function error, and the prediction horizon affects the performance of a nominal receding-horizon linear quadratic (LQ) controller. By developing a novel perturbation result of the Riccati difference equation, a novel performance upper bound is obtained and suggests that for many cases, the prediction horizon can be either one or infinity to improve the control performance, depending on the …

abstract aim analysis analyze applications arxiv control cs.lg cs.sy eess.sy error function horizon linear modeling novel performance prediction systems terminal trade trade-off type value work