28 citations · 31 across the 16 of their papers we have counts for
17 papers
Time-series Foundation Models for Predictive Control: The Role of Excitation
Mazen Amria, Jasper Hoffmann, Philipp Bordne +5
Deploying model predictive control (MPC) requires constructing or identifying a predictive model for each target system. Time-series foundation models (TSFMs) offer an attractive o…
Accelerating Reinforcement Learning via MPC Solver-Gradient Guidance for Weights-varying MPC
Baha Zarrouki, Arslan Thobani, Jasper Hoffmann +6
In Model Predictive Control (MPC), cost-function weights shape closed-loop behavior, yet changing conditions often make fixed parametrizations suboptimal and motivate context-depen…
Solving Markov Decision Processes with Future Information via MPC
Shambhuraj Sawant, Akhil S Anand, Dirk Reinhardt +1
Model Predictive Control (MPC) is widely used in industrial and robotic systems for enforcing constraints and embedding domain knowledge through finite-horizon optimization-based p…
Uncertainty Propagation under Residual Disturbances: A Smart-Home Case Study
Guanru Pan, Dirk Reinhardt, Sebastien Gros +1
This paper presents a data-driven framework for uncertainty propagation under unmeasured or statistically unmodeled (unstructured) disturbances. We consider residual disturbances,…
Cost-Matching Model Predictive Control for Efficient Reinforcement Learning in Humanoid Locomotion
Wenqi Cai, Kyriakos G. Vamvoudakis, Sébastien Gros +1
In this paper, we propose a cost-matching approach for optimal humanoid locomotion within a Model Predictive Control (MPC)-based Reinforcement Learning (RL) framework. A parameteri…
CORL: Reinforcement Learning of MILP Policies Solved via Branch and Bound
Akhil S Anand, Elias Aarekol, Martin Mziray Dalseg +2
Combinatorial sequential decision making problems are typically modeled as mixed integer linear programs (MILPs) and solved via branch and bound (B&B) algorithms. The inherent diff…
Direct transfer of optimized controllers to similar systems using dimensionless MPC
Josip Kir Hromatko, Shambhuraj Sawant, Šandor Ileš +1
Scaled model experiments are commonly used in various engineering fields to reduce experimentation costs and overcome constraints associated with full-scale systems. The relevance…
Bi-Level Reinforcement Learning Pathway for Sim-to-Real Optimality
Akhil S Anand, Shambhuraj Sawant, Paavo Parmas +3
Training Reinforcement Learning (RL) policies using simulation models before deployment in real-world environments is a common strategy when real-world interaction is expensive. Th…
Differentiable Nonlinear Model Predictive Control
Jonathan Frey, Katrin Baumgärtner, Gianluca Frison +5
The efficient computation of parametric solution sensitivities is a key challenge in the integration of learning-enhanced methods with nonlinear model predictive control (MPC), as…
Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification
Rudolf Reiter, Jasper Hoffmann, Dirk Reinhardt +6
The fields of MPC and RL consider two successful control techniques for Markov decision processes. Both approaches are derived from similar fundamental principles, and both are wid…
MPC4RL -- A Software Package for Reinforcement Learning based on Model Predictive Control
Dirk Reinhardt, Katrin Baumgärnter, Jonathan Frey +2
In this paper, we present an early software integrating Reinforcement Learning (RL) with Model Predictive Control (MPC). Our aim is to make recent theoretical contributions from th…
All AI Models are Wrong, but Some are Optimal
Akhil S Anand, Shambhuraj Sawant, Dirk Reinhardt +1
AI models that predict the future behavior of a system (a.k.a. predictive AI models) are central to intelligent decision-making. However, decision-making using predictive AI models…
Optimality Conditions for Model Predictive Control: Rethinking Predictive Model Design
Akhil S Anand, Arash Bahari Kordabad, Mario Zanon +1
Optimality is a critical aspect of Model Predictive Control (MPC), especially in economic MPC. However, achieving optimality in MPC presents significant challenges, and may even be…
Application of Soft Actor-Critic Algorithms in Optimizing Wastewater Treatment with Time Delays Integration
Esmaeel Mohammadi, Daniel Ortiz-Arroyo, Aviaja Anna Hansen +4
Wastewater treatment plants face unique challenges for process control due to their complex dynamics, slow time constants, and stochastic delays in observations and actions. These…
Economic Model Predictive Control as a Solution to Markov Decision Processes
Dirk Reinhardt, Akhil S. Anand, Shambhuraj Sawant +1
Markov Decision Processes (MDPs) offer a fairly generic and powerful framework to discuss the notion of optimal policies for dynamic systems, in particular when the dynamics are st…
Data-Driven Domestic Flexible Demand: Observations from experiments in cold climate
Dirk Reinhardt, Wenqi Cai, Sebastien Gros
In this chapter, we report on our experience with domestic flexible electric energy demand based on a regular commercial (HVAC)-based heating system in a house. Our focus is on inv…
Data-Driven Predictive Control and MPC: Do we achieve optimality?
Akhil S Anand, Shambhuraj Sawant, Dirk Reinhardt +1
In this paper, we explore the interplay between Predictive Control and closed-loop optimality, spanning from Model Predictive Control to Data-Driven Predictive Control. Predictive…