An overview of systems-theoretic guarantees in data-driven model predictive control
The development of control methods based on data has seen a surge of interest in recent
years. When applying data-driven controllers in real-world applications, providing theoretical …
years. When applying data-driven controllers in real-world applications, providing theoretical …
[HTML][HTML] Handbook of linear data-driven predictive control: Theory, implementation and design
Data-driven predictive control (DPC) has gained an increased interest as an alternative to
model predictive control in recent years, since it requires less system knowledge for …
model predictive control in recent years, since it requires less system knowledge for …
[HTML][HTML] Harnessing uncertainty for a separation principle in direct data-driven predictive control
Abstract Model Predictive Control (MPC) is a powerful method for complex system
regulation, but its reliance on an accurate model poses many limitations in real-world …
regulation, but its reliance on an accurate model poses many limitations in real-world …
Causality-informed data-driven predictive control
M Sader, Y Wang, D Huang, C Shang… - arxiv preprint arxiv …, 2023 - arxiv.org
As a useful and efficient alternative to generic model-based control scheme, data-driven
predictive control is subject to bias-variance trade-off and is known to not perform desirably …
predictive control is subject to bias-variance trade-off and is known to not perform desirably …
[PDF][PDF] Harnessing Uncertainty for a Separation Principle in Direct Data-Driven Predictive Control
Abstract Model Predictive Control (MPC) is a powerful method for complex system
regulation, but its reliance on accurate models poses many limitations in real-world …
regulation, but its reliance on accurate models poses many limitations in real-world …
Robust and efficient data-driven predictive control
We propose a robust and efficient data-driven predictive control (eDDPC) scheme which is
more sample efficient (requires less offline data) compared to existing schemes, and is also …
more sample efficient (requires less offline data) compared to existing schemes, and is also …
On the equivalence of direct and indirect data-driven predictive control approaches
Recently, several direct Data-Driven Predictive Control (DDPC) methods have been
proposed, advocating the possibility of designing predictive controllers from historical input …
proposed, advocating the possibility of designing predictive controllers from historical input …
Towards a unifying framework for data-driven predictive control with quadratic regularization
Data-driven predictive control (DPC) has recently gained popularity as an alternative to
model predictive control (MPC). Amidst the surge in proposed DPC frameworks, upon closer …
model predictive control (MPC). Amidst the surge in proposed DPC frameworks, upon closer …
DUST: A Framework for Data-Driven Density Steering
We consider the problem of data-driven stochastic optimal control of an unknown LTI
dynamical system. Assuming the process noise is normally distributed, we pose the problem …
dynamical system. Assuming the process noise is normally distributed, we pose the problem …