22 citations · 59 across the 22 of their papers we have counts for
25 papers
Efficient Uniform Feasible-Set Sampling for Approximate Linear MPC
Elias Milios, Felix Berkel, Felix Gruber +2
Model Predictive Control (MPC) offers safe and near-optimal control but suffers from high computational costs. Approximate MPC (AMPC) mitigates this by learning a cheaper surrogate…
Statistically Consistent Approximate Model Predictive Control
Elias Milios, Kim P. Wabersich, Felix Berkel +2
Model Predictive Control (MPC) offers rigorous safety and performance guarantees but is computationally intensive. Approximate MPC (AMPC) aims to circumvent this drawback by learni…
Tunable Real-Time Safety Filters via Set-Based Control Barrier Functions
Kim P. Wabersich, Felix Berkel, Felix Gruber +1
Safety filters for industrial constrained systems are required to combine certified constraint satisfaction, predictable online computation, and a transparent tuning interface. Exi…
Contract-based hierarchical control using predictive feasibility value functions
Felix Berkel, Kim Peter Wabersich, Hongxi Xiang +1
Today's control systems are often characterized by modularity and safety requirements to handle complexity, resulting in the use of hierarchical control structures. Although hierar…
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…
Stability Mechanisms for Predictive Safety Filters
Elias Milios, Kim Peter Wabersich, Felix Berkel +1
Predictive safety filters enable the integration of potentially unsafe learning-based control approaches and humans into safety-critical systems. In addition to simple constraint s…
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…
Learning Soft Constrained MPC Value Functions: Efficient MPC Design and Implementation providing Stability and Safety Guarantees
Nicolas Chatzikiriakos, Kim P. Wabersich, Felix Berkel +2
Model Predictive Control (MPC) can be applied to safety-critical control problems, providing closed-loop safety and performance guarantees. Implementation of MPC controllers requir…
LQG for Constrained Linear Systems: Indirect Feedback Stochastic MPC with Kalman Filtering
Simon Muntwiler, Kim P. Wabersich, Robert Miklos +1
We present an output feedback stochastic model predictive control (SMPC) approach for linear systems subject to Gaussian disturbances and measurement noise and probabilistic constr…
Approximate Predictive Control Barrier Functions using Neural Networks: A Computationally Cheap and Permissive Safety Filter
Alexandre Didier, Robin C. Jacobs, Jerome Sieber +2
A predictive control barrier function (PCBF) based safety filter is a modular framework to verify safety of a control input by predicting a future trajectory. The approach relies o…
State space models vs. multi-step predictors in predictive control: Are state space models complicating safe data-driven designs?
Johannes Köhler, Kim P. Wabersich, Julian Berberich +1
This paper contrasts recursive state space models and direct multi-step predictors for linear predictive control. We provide a tutorial exposition for both model structures to solv…
Adaptive Model Predictive Safety Certification for Learning-based Control -- Extended Version
Alexandre Didier, Kim P. Wabersich, Melanie N. Zeilinger
We propose an adaptive Model Predictive Safety Certification (MPSC) scheme for learning-based control of linear systems with bounded disturbances and uncertain parameters where the…
Learning-based Moving Horizon Estimation through Differentiable Convex Optimization Layers
Simon Muntwiler, Kim P. Wabersich, Melanie N. Zeilinger
To control a dynamical system it is essential to obtain an accurate estimate of the current system state based on uncertain sensor measurements and existing system knowledge. An op…
Predictive control barrier functions: Enhanced safety mechanisms for learning-based control
Kim P. Wabersich, Melanie N. Zeilinger
While learning-based control techniques often outperform classical controller designs, safety requirements limit the acceptance of such methods in many applications. Recent develop…
A predictive safety filter for learning-based racing control
Ben Tearle, Kim P. Wabersich, Andrea Carron +1
The growing need for high-performance controllers in safety-critical applications like autonomous driving has been motivating the development of formal safety verification techniqu…
Bayesian model predictive control: Efficient model exploration and regret bounds using posterior sampling
Kim P. Wabersich, Melanie N. Zeilinger
Tight performance specifications in combination with operational constraints make model predictive control (MPC) the method of choice in various industries. As the performance of a…
Cautious Bayesian MPC: Regret Analysis and Bounds on the Number of Unsafe Learning Episodes
Kim P. Wabersich, Melanie N. Zeilinger
This paper investigates the combination of model predictive control (MPC) concepts and posterior sampling techniques and proposes a simple constraint tightening technique to introd…
Data-Driven Distributed Stochastic Model Predictive Control with Closed-Loop Chance Constraint Satisfaction
Simon Muntwiler, Kim P. Wabersich, Lukas Hewing +1
Distributed model predictive control methods for uncertain systems often suffer from considerable conservatism and can tolerate only small uncertainties due to the use of robust fo…
Distributed Model Predictive Safety Certification for Learning-based Control
Simon Muntwiler, Kim P. Wabersich, Andrea Carron +1
While distributed algorithms provide advantages for the control of complex large-scale systems by requiring a lower local computational load and less local memory, it is a challeng…
Probabilistic model predictive safety certification for learning-based control
Kim P. Wabersich, Lukas Hewing, Andrea Carron +1
Reinforcement learning (RL) methods have demonstrated their efficiency in simulation environments. However, many applications for which RL offers great potential, such as autonomou…
Recursively Feasible Stochastic Model Predictive Control using Indirect Feedback
Lukas Hewing, Kim P. Wabersich, Melanie N. Zeilinger
We present a stochastic model predictive control (MPC) method for linear discrete-time systems subject to possibly unbounded and correlated additive stochastic disturbance sequence…
A predictive safety filter for learning-based control of constrained nonlinear dynamical systems
Kim P. Wabersich, Melanie N. Zeilinger
The transfer of reinforcement learning (RL) techniques into real-world applications is challenged by safety requirements in the presence of physical limitations. Most RL methods, i…
Linear model predictive safety certification for learning-based control
Kim P. Wabersich, Melanie N. Zeilinger
While it has been repeatedly shown that learning-based controllers can provide superior performance, they often lack of safety guarantees. This paper aims at addressing this proble…
Scalable synthesis of safety certificates from data with application to learning-based control
Kim P. Wabersich, Melanie N. Zeilinger
The control of complex systems faces a trade-off between high performance and safety guarantees, which in particular restricts the application of learning-based methods to safety-c…