12 citations · 14 across the 10 of their papers we have counts for
11 papers
Local Observability and Moving Horizon Estimation-based Training of Feedforward Neural Networks
Yi Yang, Victor G. Lopez, Matthias A. Müller
In this paper, we propose a moving horizon estimation (MHE)-based training method for feedforward neural networks (FNNs) with rectified linear unit (ReLU) activation functions to d…
Beyond Shrinkage: Foundations of Data-Driven Control for Piecewise Affine Systems
Gianluca Giacomelli, Victor G. Lopez, Simone Formentin +2
Data-enabled predictive control (DeePC) has recently attracted attention as a promising approach for controlling systems directly from raw data, without requiring an explicit ident…
An Output Feedback Q-learning Algorithm for Optimal Control of Nonlinear Systems with Koopman Linear Embedding
Victor G. Lopez, Malte Heinrich, Matthias A. Müller
In the reinforcement learning literature, strong theoretical guarantees have been obtained for algorithms applicable to LTI systems. However, in the nonlinear case only weaker resu…
Sample-based detectability and moving horizon state estimation of continuous-time systems
Isabelle Krauss, Victor G. Lopez, Matthias A. Müller
In this paper we propose a detectability condition for nonlinear continuous-time systems with irregular/infrequent output measurements, namely a sample-based version of incremental…
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…
Sample-based Moving Horizon Estimation
Isabelle Krauss, Victor G. Lopez, Matthias A. Müller
In this paper, we propose a sample-based moving horizon estimation (MHE) scheme for general nonlinear systems to estimate the current system state using irregularly and/or infreque…
Data-Based Control of Continuous-Time Linear Systems with Performance Specifications
Victor G. Lopez, Matthias A. Müller
The design of direct data-based controllers has become a fundamental part of control theory research in the last few years. In this paper, we consider three classes of data-based s…
Gaussian Process-Based Nonlinear Moving Horizon Estimation
Tobias M. Wolff, Victor G. Lopez, Matthias A. Müller
In this paper, we propose a novel Gaussian process-based moving horizon estimation (MHE) framework for unknown nonlinear systems. On the one hand, we approximate the system dynamic…
Sample-based nonlinear detectability for discrete-time systems
Isabelle Krauss, Victor G. Lopez, Matthias A. Müller
This paper introduces two sample-based formulations of incremental input/output-to-state stability (i-IOSS), a suitable detectability notion for general nonlinear systems. In this…
Notes on data-driven output-feedback control of linear MIMO systems
Mohammad Alsalti, Victor G. Lopez, Matthias A. Müller
Recent works have approached the data-driven design of dynamic output-feedback controllers for discrete-time LTI systems by constructing non-minimal state vectors composed of past…
Sample- and computationally efficient data-driven predictive control
Mohammad Alsalti, Manuel Barkey, Victor G. Lopez +1
Recently proposed data-driven predictive control schemes for LTI systems use non-parametric representations based on the image of a Hankel matrix of previously collected, persisten…