5 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…