5 papers · 1 filter
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…
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…
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…