14 citations · 16 across the 8 of their papers we have counts for
8 papers
Tube MPC for Bilinear Koopman Models using Robust Control Contraction Metrics
Thomas de Jong, Mircea Lazar
This paper presents a robust tube model predictive control (MPC) framework for nonlinear systems represented by bilinear Koopman models identified from data. We derive discrete-tim…
Offset-free Data-Driven Predictive Control for Grid-Connected Power Converters in Weak Grid Faults
Ivo Kraayeveld, Thomas de Jong, Mircea Lazar
Grid-connected power converters encounter significant stability challenges during weak grid faults, when conventional PI-based controllers exhibit an oscillatory response and poor…
Deep Operator Neural Network Model Predictive Control
Thomas Oliver de Jong, Khemraj Shukla, Mircea Lazar
In this paper, we consider the design of model predictive control (MPC) algorithms based on deep operator neural networks (DeepONets). These neural networks are capable of accurate…
A Kernelized Operator Approach to Nonlinear Data-Enabled Predictive Control
Thomas de Jong, Siep Weiland, Mircea Lazar
This paper considers the design of nonlinear data-enabled predictive control (DeePC) using kernel functions. Compared with existing methods that use kernels to parameterize multi-s…
Kernelized offset-free data-driven predictive control for nonlinear systems
Thomas Oliver de Jong, Mircea Lazar
This paper presents a kernelized offset-free data-driven predictive control scheme for nonlinear systems. Traditional model-based and data-driven predictive controllers often strug…
Neural Data-Enabled Predictive Control
Mircea Lazar
Data-enabled predictive control (DeePC) for linear systems utilizes data matrices of recorded trajectories to directly predict new system trajectories, which is very appealing for…
Koopman Data-Driven Predictive Control with Robust Stability and Recursive Feasibility Guarantees
Thomas de Jong, Valentina Breschi, Maarten Schoukens +1
In this paper, we consider the design of data-driven predictive controllers for nonlinear systems from input-output data via linear-in-control input Koopman lifted models. Instead…
Model predictive eco-driving control for heavy-duty trucks using Branch and Bound optimization
B. Wingelaar, G. R. Gonçalves da Silva, M. Lazar
Eco-driving (ED) can be used for fuel savings in existing vehicles, requiring only a few hardware modifications. For this technology to be successful in a dynamic environment, ED r…