
The core message of this article is to provide a novel suboptimality analysis of a nominal receding-horizon linear quadratic (LQ) controller under the joint effect of modeling error, terminal value function error, and prediction horizon. The analysis reveals that for many cases, the prediction horizon can be either 1 or infinity to improve the control performance, depending on the relative difference between the modeling error and the terminal value function error.


coremsg

suboptimal-performance-of-receding-horizon-control-with-unknown-linear-systems-and-its-applications-in-learning-based-control


Suboptimal Performance of Receding Horizon Control with Unknown Linear Systems and Its Applications in Learning-Based Control
