1 citations · 1 across the 6 of their papers we have counts for
6 papers
Tuning the burn-in phase in training recurrent neural networks improves their performance
Julian D. Schiller, Malte Heinrich, Victor G. Lopez +1
Training recurrent neural networks (RNNs) with standard backpropagation through time (BPTT) can be challenging, especially in the presence of long input sequences. A practical alte…
Distributed Model Predictive Control for Dynamic Cooperation of Multi-Agent Systems
Matthias Köhler, Matthias A. Müller, Frank Allgöwer
We propose a distributed model predictive control (MPC) framework for coordinating heterogeneous, nonlinear multi-agent systems under individual and coupling constraints. The coope…
Multi-objective robust controller synthesis with integral quadratic constraints in discrete-time
Lukas Schwenkel, Johannes Köhler, Matthias A. Müller +2
This article presents a novel framework for the robust controller synthesis problem in discrete-time systems using dynamic Integral Quadratic Constraints (IQCs). We present an algo…
Robust and efficient data-driven predictive control
Mohammad Alsalti, Manuel Barkey, Victor G. Lopez +1
We propose a robust and efficient data-driven predictive control (eDDPC) scheme which is more sample efficient (requires less offline data) compared to existing schemes, and is als…
An input-output continuous-time version of Willems' lemma
Victor G. Lopez, Matthias A. Müller, Paolo Rapisarda
We illustrate a novel version of Willems' lemma for data-based representation of continuous-time systems. The main novelties compared to previous works are two. First, the proposed…
On discount functions for economic model predictive control without terminal conditions
Lukas Schwenkel, Daniel Briem, Matthias A. Müller +1
In this paper, we investigate discounted economic model predictive control (E-MPC) schemes without terminal conditions in scenarios where the optimal operating behavior is a period…