6 papers
The Separation Principle and the Dual-Certainty Equivalence Gap in Model Predictive Control
Tren Baltussen, Nathan P. Lawrence, Alexander Katriniok +2
Dual control addresses the trade-off between exploitation and exploration, where control inputs both regulate the system and generate informative data for estimation and identifica…
Value Function Approximation for Nonlinear MPC: Learning a Terminal Cost Function with a Descent Property
T. M. J. T. Baltussen, C. A. Orrico, A. Katriniok +2
We present a novel method to synthesize a terminal cost function for a nonlinear model predictive controller (MPC) through value function approximation using supervised learning. E…
Dual MPC for Active Learning of Nonparametric Uncertainties
Tren Baltussen, Maurice Heemels, Alexander Katriniok
This manuscript presents a dual model predictive controller (MPC) that balances the two objectives of dual control, namely, system identification and control. In particular, we pro…
Data-Driven Stabilization Using Prior Knowledge on Stabilizability and Controllability
Amir Shakouri, Henk J. van Waarde, Tren M. J. T. Baltussen +1
In this work, we study data-driven stabilization of linear time-invariant systems using prior knowledge of system-theoretic properties, specifically stabilizability and controllabi…
Online Learning of Interaction Dynamics with Dual Model Predictive Control for Multi-Agent Systems Using Gaussian Processes
T. M. J. T. Baltussen, A. Katriniok, E. Lefeber +2
The control of a single agent in complex and uncertain multi-agent environments requires careful consideration of the interactions between the agents. In this context, this paper p…
A Contingency Model Predictive Control Framework for Safe Learning
Merlijne Geurts, Tren Baltussen, Alexander Katriniok +1
This research introduces a multi-horizon contingency model predictive control (CMPC) framework in which classes of robust MPC (RMPC) algorithms are combined with classes of learnin…