6 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…
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…
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…
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…