Safe learning in robotics: From learning-based control to safe reinforcement learning

L Brunke, M Greeff, AW Hall, Z Yuan… - Annual Review of …, 2022 - annualreviews.org
The last half decade has seen a steep rise in the number of contributions on safe learning
methods for real-world robotic deployments from both the control and reinforcement learning …

Learning-based model predictive control: Toward safe learning in control

L Hewing, KP Wabersich, M Menner… - Annual Review of …, 2020 - annualreviews.org
Recent successes in the field of machine learning, as well as the availability of increased
sensing and computational capabilities in modern control systems, have led to a growing …

A computationally efficient robust model predictive control framework for uncertain nonlinear systems

J Köhler, R Soloperto, MA Müller… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
In this article, we present a nonlinear robust model predictive control (MPC) framework for
general (state and input dependent) disturbances. This approach uses an online …

Advanced model predictive control framework for autonomous intelligent mechatronic systems: A tutorial overview and perspectives

Y Shi, K Zhang - Annual Reviews in Control, 2021 - Elsevier
This paper presents a review on the development and application of model predictive
control (MPC) for autonomous intelligent mechatronic systems (AIMS). Starting from the …

Fusion of machine learning and MPC under uncertainty: What advances are on the horizon?

A Mesbah, KP Wabersich, AP Schoellig… - 2022 American …, 2022 - ieeexplore.ieee.org
This paper provides an overview of the recent research efforts on the integration of machine
learning and model predictive control under uncertainty. The paper is organized as a …

Data-driven methods for building control—A review and promising future directions

ET Maddalena, Y Lian, CN Jones - Control Engineering Practice, 2020 - Elsevier
A review of the heating, ventilation and air-conditioning control problem for buildings is
presented with particular emphasis on its distinguishing features. Next, we not only examine …

[HTML][HTML] Robust adaptive MPC using control contraction metrics

A Sasfi, MN Zeilinger, J Köhler - Automatica, 2023 - Elsevier
We present a robust adaptive model predictive control (MPC) framework for nonlinear
continuous-time systems with bounded parametric uncertainty and additive disturbance. We …

Robust min-max model predictive vehicle platooning with causal disturbance feedback

J Zhou, D Tian, Z Sheng, X Duan, G Qu… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Platoon-based vehicular cyber-physical systems have gained increasing attention due to
their potentials in improving traffic efficiency, capacity, and saving energy. However, external …

Trajectory tracking control of autonomous ground vehicles using adaptive learning MPC

K Zhang, Q Sun, Y Shi - IEEE Transactions on Neural Networks …, 2021 - ieeexplore.ieee.org
In this work, an adaptive learning model predictive control (ALMPC) scheme is proposed for
the trajectory tracking of perturbed autonomous ground vehicles (AGVs) subject to input …

Integrated battery thermal and energy management for electric vehicles with hybrid energy storage system: A hierarchical approach

Y Wu, Z Huang, D Li, H Li, J Peng, JM Guerrero… - Energy Conversion and …, 2024 - Elsevier
Battery cooling is crucial for electric vehicles' thermal safety, energy consumption, and
battery life in hot climatic conditions. For electric vehicles with battery/supercapacitor hybrid …