[HTML][HTML] Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment
Currently, a significant effort in the world research panorama is focused on finding efficient
solutions to a carbon-free energy supply, wave energy being one of the most promising …
solutions to a carbon-free energy supply, wave energy being one of the most promising …
Robust tube-based model predictive control with Koopman operators
Koopman operators are of infinite dimension and capture the characteristics of nonlinear
dynamics in a lifted global linear manner. The finite data-driven approximation of Koopman …
dynamics in a lifted global linear manner. The finite data-driven approximation of Koopman …
The data-driven approach to classical control theory
A data-driven approach to control design has been developing, since the early 1990's, upon
the concepts and the methods of classical control theory; to this approach we refer as data …
the concepts and the methods of classical control theory; to this approach we refer as data …
Performance-oriented model learning for data-driven MPC design
Model predictive control (MPC) is an enabling technology in applications requiring
controlling physical processes in an optimized way under constraints on inputs and outputs …
controlling physical processes in an optimized way under constraints on inputs and outputs …
Design and experimental evaluation of an efficient MPC-based lateral motion controller considering path preview for autonomous vehicles
G Chen, J Yao, H Hu, Z Gao, L He, X Zheng - Control engineering practice, 2022 - Elsevier
Lateral motion control, a core autonomous driving technology, still faces the significant
challenge of accurately tracking the reference path under complex and changeable driving …
challenge of accurately tracking the reference path under complex and changeable driving …
Ultra-local model predictive control: A model-free approach and its application on automated vehicle trajectory tracking
Abstract Model predictive control (MPC) has been extensively utilized in the automotive
applications, such as autonomous vehicle path planning and control, hybrid-vehicle energy …
applications, such as autonomous vehicle path planning and control, hybrid-vehicle energy …
Robust learning-based MPC for nonlinear constrained systems
This paper presents a robust learning-based predictive control strategy for nonlinear
systems subject to both input and output constraints, under the assumption that the model …
systems subject to both input and output constraints, under the assumption that the model …
Cooperative fuzzy-neural control for wastewater treatment process
H Han, H Liu, J Li, J Qiao - IEEE Transactions on Industrial …, 2020 - ieeexplore.ieee.org
Wastewater treatment process, including multiple biochemical reactions, is a complex
industrial process with strong nonlinearity and time-varying dynamics. It is a challenge to …
industrial process with strong nonlinearity and time-varying dynamics. It is a challenge to …
A data-driven tracking control framework using physics-informed neural networks and deep reinforcement learning for dynamical systems
RR Faria, BDO Capron, AR Secchi… - … Applications of Artificial …, 2024 - Elsevier
This paper addresses how physical knowledge can improve machine learning in process
control. A data-driven tracking control framework using physics-informed neural networks …
control. A data-driven tracking control framework using physics-informed neural networks …
Adaptive neural network control for time-varying state constrained nonlinear stochastic systems with input saturation
Q Zhu, Y Liu, G Wen - Information Sciences, 2020 - Elsevier
This paper investigates the tracking control issue of nonlinear stochastic systems subject to
time-varying full state constraints and input saturation. By employing both neural network …
time-varying full state constraints and input saturation. By employing both neural network …