571 citations · 1.3k across the 123 of their papers we have counts for
24 papers · 1 filter
Adaptive Economic Model Predictive Control: Performance Guarantees for Nonlinear Systems
Maximilian Degner, Raffaele Soloperto, Melanie N. Zeilinger +2
We consider the problem of optimizing the economic performance of nonlinear constrained systems subject to uncertain time-varying parameters and bounded disturbances. In particular…
Moving Horizon Estimation for Simultaneous Localization and Mapping with Robust Estimation Error Bounds
Jelena Trisovic, Alexandre Didier, Simon Muntwiler +1
This paper presents a robust moving horizon estimation (MHE) approach with provable estimation error bounds for solving the simultaneous localization and mapping (SLAM) problem. We…
L4acados: Learning-based models for acados, applied to Gaussian process-based predictive control
Amon Lahr, Joshua Näf, Kim P. Wabersich +5
Incorporating learning-based models, such as artificial neural networks or Gaussian processes, into model predictive control (MPC) strategies can significantly improve control perf…
Approximate predictive control barrier function for discrete-time systems
Alexandre Didier, Melanie N. Zeilinger
We propose integrating an approximation of a predictive control barrier function (PCBF) in a safety filter framework, resulting in a prediction horizon independent formulation. The…
Lambda-Skip Connections: the architectural component that prevents Rank Collapse
Federico Arangath Joseph, Jerome Sieber, Melanie N. Zeilinger +1
Rank collapse, a phenomenon where embedding vectors in sequence models rapidly converge to a uniform token or equilibrium state, has recently gained attention in the deep learning…
Towards safe and tractable Gaussian process-based MPC: Efficient sampling within a sequential quadratic programming framework
Manish Prajapat, Amon Lahr, Johannes Köhler +2
Learning uncertain dynamics models using Gaussian process~(GP) regression has been demonstrated to enable high-performance and safety-aware control strategies for challenging real-…
Stochastic Data-Driven Predictive Control: Chance-Constraint Satisfaction with Identified Multi-step Predictors
Haldun Balim, Andrea Carron, Melanie N. Zeilinger +1
We propose a novel data-driven stochastic model predictive control framework for uncertain linear systems with noisy output measurements. Our approach leverages multi-step predicto…
On the role of the signature transform in nonlinear systems and data-driven control
Anna Scampicchio, Melanie N. Zeilinger
Classic control techniques typically rely on a model of the system's response to external inputs, which is difficult to obtain from first principles especially if the unknown dynam…
From Data to Predictive Control: A Framework for Stochastic Linear Systems with Output Measurements
Haldun Balim, Andrea Carron, Melanie N. Zeilinger +1
We introduce data to predictive control, D2PC, a framework to facilitate the design of robust and predictive controllers from data. The proposed framework is designed for discrete-…
Predictive control for nonlinear stochastic systems: Closed-loop guarantees with unbounded noise
Johannes Köhler, Melanie N. Zeilinger
We present a stochastic model predictive control framework for nonlinear systems subject to unbounded process noise with closed-loop guarantees. First, we provide a conceptual shri…
Computationally Efficient System Level Tube-MPC for Uncertain Systems
Jerome Sieber, Alexandre Didier, Melanie N. Zeilinger
Tube-based model predictive control (MPC) is one of the principal robust control techniques for constrained linear systems affected by additive disturbances. While tube-based metho…
Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation
Pablo Krupa, Johannes Köhler, Antonio Ferramosca +4
This paper provides a comprehensive tutorial on a family of Model Predictive Control (MPC) formulations, known as MPC for tracking, which are characterized by including an artifici…
Understanding the differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks
Jerome Sieber, Carmen Amo Alonso, Alexandre Didier +2
Softmax attention is the principle backbone of foundation models for various artificial intelligence applications, yet its quadratic complexity in sequence length can limit its inf…
Optimization-Based System Identification and Moving Horizon Estimation Using Low-Cost Sensors for a Miniature Car-Like Robot
Sabrina Bodmer, Lukas Vogel, Simon Muntwiler +5
This paper presents an open-source miniature car-like robot with low-cost sensing and a pipeline for optimization-based system identification, state estimation, and control. The ov…
Stochastic Online Optimization for Cyber-Physical and Robotic Systems
Hao Ma, Melanie Zeilinger, Michael Muehlebach
We propose a novel gradient-based online optimization framework for solving stochastic programming problems that frequently arise in the context of cyber-physical and robotic syste…
Perfecting Periodic Trajectory Tracking: Model Predictive Control with a Periodic Observer (-MPC)
Luis Pabon, Johannes Köhler, John Irvin Alora +4
In Model Predictive Control (MPC), discrepancies between the actual system and the predictive model can lead to substantial tracking errors and significantly degrade performance an…
State Space Models as Foundation Models: A Control Theoretic Overview
Carmen Amo Alonso, Jerome Sieber, Melanie N. Zeilinger
In recent years, there has been a growing interest in integrating linear state-space models (SSM) in deep neural network architectures of foundation models. This is exemplified by…
Adaptive Economic Model Predictive Control for linear systems with performance guarantees
Maximilian Degner, Raffaele Soloperto, Melanie N. Zeilinger +2
We present a model predictive control (MPC) formulation to directly optimize economic criteria for linear constrained systems subject to disturbances and uncertain model parameters…
MPCC++: Model Predictive Contouring Control for Time-Optimal Flight with Safety Constraints
Maria Krinner, Angel Romero, Leonard Bauersfeld +3
Quadrotor flight is an extremely challenging problem due to the limited control authority encountered at the limit of handling. Model Predictive Contouring Control (MPCC) has emerg…
Safe Guaranteed Exploration for Non-linear Systems
Manish Prajapat, Johannes Köhler, Matteo Turchetta +2
Safely exploring environments with a-priori unknown constraints is a fundamental challenge that restricts the autonomy of robots. While safety is paramount, guarantees on sufficien…
Inherently robust suboptimal MPC for autonomous racing with anytime feasible SQP
Logan Numerow, Andrea Zanelli, Andrea Carron +1
In recent years, the increasing need for high-performance controllers in applications like autonomous driving has motivated the development of optimization routines tailored to spe…
Probabilistic ODE Solvers for Integration Error-Aware Numerical Optimal Control
Amon Lahr, Filip Tronarp, Nathanael Bosch +3
Appropriate time discretization is crucial for real-time applications of numerical optimal control, such as nonlinear model predictive control. However, if the discretization error…
Fast System Level Synthesis: Robust Model Predictive Control using Riccati Recursions
Antoine P. Leeman, Johannes Köhler, Florian Messerer +3
System level synthesis enables improved robust MPC formulations by allowing for joint optimization of the nominal trajectory and controller. This paper introduces a tailored algori…
Predictive stability filters for nonlinear dynamical systems affected by disturbances
Alexandre Didier, Andrea Zanelli, Kim P. Wabersich +1
Predictive safety filters provide a way of projecting potentially unsafe inputs, proposed, e.g. by a human or learning-based controller, onto the set of inputs that guarantee recur…