310 citations · 568 across the 17 of their papers we have counts for
20 papers
Improved uncertainty quantification for neural networks with Bayesian last layer
Felix Fiedler, Sergio Lucia
Uncertainty quantification is an important task in machine learning - a task in which standardneural networks (NNs) have traditionally not excelled. This can be a limitation for sa…
Resilient Model Predictive Control of Distributed Systems Under Attack Using Local Attack Identification
Sarah Braun, Sebastian Albrecht, Sergio Lucia
With the growing share of renewable energy sources, the uncertainty in power supply is increasing. In addition to the inherent fluctuations in the renewables, this is due to the th…
Optimization-Based Predictive Congestion Control for the Tor Network: Opportunities and Challenges
Christoph Döpmann, Felix Fiedler, Sergio Lucia +1
Based on the principle of onion routing, the Tor network achieves anonymity for its users by relaying user data over a series of intermediate relays. This approach makes congestion…
On the Practical Design of Tube-Enhanced Multi-Stage Nonlinear Model Predictive Control
Sankaranarayanan Subramanian, Yehia Abdelsalam, Sergio Lucia +1
Tube-enhanced multi-stage nonlinear model predictive control is a robust control scheme that can handle a wide range of uncertainties with reduced conservatism and manageable compu…
Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}
Jong Woo Kim, Niels Krausch, Judit Aizpuru +4
We discuss the application of a nonlinear model predictive control (MPC) and a moving horizon estimation (MHE) to achieve an optimal operation of \textit{E. coli} fed-batch cultiva…
Model predictive control guided with optimal experimental design for pulse-based parallel cultivation
Jong Woo Kim, Niels Krausch, Judit Aizpuru +5
Optimal experimental design for parameter precision attempts to maximize the information content in experimental data for a most effective identification of parametric model. With…
Teaching MPC: Which Way to the Promised Land?
Timm Faulwasser, Sergio Lucia, Moritz Schulze Darup +1
Since the earliest conceptualizations by Lee and Markus, and Propoi in the 1960s, Model Predictive Control (MPC) has become a major success story of systems and control with respec…
A Polynomial Chaos Approach to Robust Static Output-Feedback Control with Bounded Truncation Error
Yiming Wan, Dongying E. Shen, Sergio Lucia +2
This article considers the static output-feedback control for linear time-invariant uncertain systems with polynomial dependence on probabilistic time-invarian…
Tube-enhanced Multi-stage MPC for Flexible Robust Control of Constrained Linear Systems with Additive and Parametric Uncertainties
Sankaranarayanan Subramanian, Sergio Lucia, Radoslav Paulen +1
The trade-off between optimality and complexity has been one of the most important challenges in the field of robust Model Predictive Control (MPC). To address the challenge, we pr…
On the relationship between data-enabled predictive control and subspace predictive control
Felix Fiedler, Sergio Lucia
Data-enabled predictive control (DeePC) is a recently proposed approach that combines system identification, estimation and control in a single optimization problem, for which only…
A Hierarchical Attack Identification Method for Nonlinear Systems
Sarah Braun, Sebastian Albrecht, Sergio Lucia
Many autonomous control systems are frequently exposed to attacks, so methods for attack identification are crucial for a safe operation. To preserve the privacy of the subsystems…
Sensor Artificial Intelligence and its Application to Space Systems -- A White Paper
Anko Börner, Heinz-Wilhelm Hübers, Odej Kao +15
Information and communication technologies have accompanied our everyday life for years. A steadily increasing number of computers, cameras, mobile devices, etc. generate more and…
PredicTor: Predictive Congestion Control for the Tor Network
Felix Fiedler, Christoph Döpmann, Florian Tschorsch +1
In the Tor network, anonymity is achieved through a multi-layered architecture, which comes at the cost of a complex network. Scheduling data in this network is a challenging task…
Stability and feasibility of neural network-based controllers via output range analysis
Benjamin Karg, Sergio Lucia
Neural networks can be used as approximations of several complex control schemes such as model predictive control. We show in this paper which properties deep neural networks with…
A probabilistic validation approach for penalty function design in Stochastic Model Predictive Control
Martina Mammarella, Teodoro Alamo, Sergio Lucia +1
In this paper, we consider a stochastic Model Predictive Control able to account for effects of additive stochastic disturbance with unbounded support, and requiring no restrictive…
Sparse Magnetometer-free Inertial Motion Tracking -- A Condition for Observability in Double Hinge Joint Systems
Karsten Eckhoff, Manon Kok, Sergio Lucia +1
Inertial measurement units are commonly used in a growing number of application fields to track or capture motions of kinematic chains, such as human limbs, exoskeletons or robotic…
Probabilistic performance validation of deep learning-based robust NMPC controllers
Benjamin Karg, Teodoro Alamo, Sergio Lucia
Solving nonlinear model predictive control problems in real time is still an important challenge despite of recent advances in computing hardware, optimization algorithms and tailo…
Deep Learning-Based Model Predictive Control for Resonant Power Converters
Sergio Lucia, Denis Navarro, Benjamin Karg +2
Resonant power converters offer improved levels of efficiency and power density. In order to implement such systems, advanced control techniques are required to take the most of th…
Efficient representation and approximation of model predictive control laws via deep learning
Benjamin Karg, Sergio Lucia
We show that artificial neural networks with rectifier units as activation functions can exactly represent the piecewise affine function that results from the formulation of model…
Modeling metabolic networks including gene expression and uncertainties
Henning Lindhorst, Sergio Lucia, Rolf Findeisen +1
Constraint based methods, such as the Flux Balance Analysis, are widely used to model cellular growth processes without relying on extensive information on the regulatory features.…