29 citations · 120 across the 27 of their papers we have counts for
6 papers · 1 filter
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…
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…
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-…
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…
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…
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…