Chance-Constrained Control

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17 papers4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

A Bayesian Framework for Safe Optimal Control of Unknown Systems from Input-Output Data
Nov 2, 2026Robert Lefringhausen, Annalena Daniels, Michael Fink +1Optimal Control Safety-Critical Control
School of Computation, Information and Technology, Technical University of Munich, Germany · NTFE GmbH, Munich, Germany
Rare Event Constrained Reinforcement Learning with Large Deviations
Nov 1, 2026Rahul MisraReinforcement Learning Constrained RL
Department of Electronic Systems, Automation and Control section, Aalborg University, Fredrik Bajers Vej 7 C, Aalborg East, 9220, Denmark
Chance-Constrained Inspection Viewpoint Sequencing for On-Orbit Servicing via Safe Reinforcement Learning
Oct 30, 2026Chloé Carer, Adam AbdinRobotic Inspection Next-Best-View Planning
LGI
socx: GPU-Accelerated Chance-Constrained Stochastic Optimal Control using JAX
Oct 29, 2026Tanmay Dokania, Yashwanth Kumar NakkaChance-Constrained Control Stochastic Optimal Control
Authors are with the Daniel Guggenheim School of Aerospace Engineering at the Georgia Institute of Technology, Atlanta, GA, 30332 USA.
Policy Synthesis for Finite Populations of MDP Agents under Aggregate Reach-Avoid Chance Constraints
Oct 8, 2026Jie Fu, Anamika DubeyMulti-Agent Control Chance-Constrained Control
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA · School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA
Learning Chance-Constrained MDPs with Bellman Distributional Certificates
Sep 25, 2026Chenbei Lu, Hongyu YiMarkov Decision Processes Constrained RL
Cornell University AI for Science Institute Cornell University Ithaca, NY, USA · Department of Electrical & Computer Engineering University of Washington Seattle, WA, USA
Higher-Order Approximation of Exit Functionals in Sampling-Based Stochastic Model Predictive Control
Sep 21, 2026Sashank Modali, Takashi TanakaSafety-Critical Control Stochastic Optimal Control
School of Aeronautics and Astronautics, Purdue University, IN 47906
Learning Agent-based Model Predictive Control for Holistic Vehicle Performance
Sep 10, 2026Jiaming Zhong, Reza Valiollahi Mehrizi, Mohammad Pirani +4Multi-Agent Control Model Predictive Control
Mechatronic Vehicle Systems (MVS) Lab, Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Ave West, Waterloo ON, N2L3G1 Canada · Department of Mechanical Engineering, University of Ottawa, 75 Laurier Ave East, Ottawa, ON, K1N 6N5 Canada · General Motors Company, Warren, MI 48090 USA +1
Learning-Based Stochastic Optimal Control with Infinite-Horizon Probabilistic Constraints
Aug 2, 2026Francesco Cordiano, Kanghui He, Bart De SchutterChance-Constrained Optimization Markov Decision Processes
Delft Center for Systems and Control, Delft University of Technology, 2628 CD, Delft, The Netherlands · Department of Engineering Science, University of Oxford, OX1 3PJ Oxford, U.K.
Conformal Constraint Tightening for Chance-Constrained Motion Planning with Unknown Dynamics
Jul 24, 2026Shubham Natraj, Bruno Sinopoli, Yiannis KantarosConstrained Motion Planning Chance-Constrained Motion Planning
Department of Electrical and Systems Engineering, Washington University in St. Louis, 1 Brookings Dr, St. Louis, MO 63130, USA · School of Electrical, Computer and Energy Engineering, Arizona State University, 650 E. Tyler Mall, Tempe, AZ 85281, USA
Relative Entropy-Bounded Ambiguous Chance Constraints for Robust Planning in Nonlinear Systems
Jul 18, 2026Trevor N. Wolf, Jay W. McMahonChance-Constrained Optimization Stochastic Optimal Control
Smead Department of Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, CO 80303, USA
Distribution-Agnostic Robust Trajectory Optimization via Chance-Constrained Reinforcement Learning
Jun 11, 2026Yashdeep Chaudhary, Roberto Armellin, Harry Holt +1Trajectory Optimization Constrained RL
Faculty of Engineering, Waipapa Taumata Rau – University of Auckland, 20 Symonds Street, Auckland, 1010, New Zealand · Advanced Concepts Team, European Space Research and Technology Centre, ESA, Noordwijk, 2201 AZ, The Netherlands · Department of Industrial Engineering, Alma Mater Studiorum - Università di Bologna, Via Zamboni 33, Bologna, 40126, Italy
Iterative Model-Learning Scheme via Gaussian Processes for Nonlinear Model Predictive Control of (Semi-)Batch Processes
Apr 24, 2026Tai Xuan Tan, Alexander Mitsos, Eike CramerNonlinear MPC Stochastic Optimal Control
Process Systems Engineering (AVT.SVT), RWTH Aachen University, Forckenbeckstraße 51, 52074 Aachen, Germany · JARA-CSD, 52056 Aachen, Germany · Institute of Climate and Energy Systems: Energy Systems Engineering (ICE-1), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany +1
Rate-Optimal Regret for the Safe Learning-based Control of the Constrained Linear Quadratic Regulator
Apr 24, 2026Spencer Hutchinson, Nanfei Jiang, Mahnoosh AlizadehStochastic Linear Dynamical Systems Stochastic Optimal Control
Department of Electrical and Computer Engineering, University of California Santa Barbara
Exact and Approximate Convex Reformulation of Linear Stochastic Optimal Control with Chance Constraints
Mar 19, 2026Tanmay Dokania, Yashwanth Kumar NakkaChance-Constrained Control Stochastic Optimal Control
Authors are with the Daniel Guggenheim School of Aerospace Engineering at the Georgia Institute of Technology, Atlanta, GA, 30332 USA.
Statistical Contraction for Chance-Constrained Trajectory Optimization of Non-Gaussian Stochastic Systems
Mar 7, 2026Rihan Aaron D'Silva, Hiroyasu TsukamotoContraction Theory Trajectory Optimization
Department of Aerospace Engineering, The Grainger College of Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801
Convex Chance-Constrained Stochastic Control under Uncertain Specifications with Application to Learning-Based Hybrid Powertrain Control
Jan 26, 2026Teruki Kato, Ryotaro Shima, Kenji KashimaChance-Constrained Control Chance-Constrained Optimization
Toyota Central R&D Labs., Inc., Nagakute, Aichi, Japan · Graduate School of Informatics, Kyoto University, Kyoto, Japan

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A Bayesian Framework for Safe Optimal Control of Unknown Systems from Input-Output Data

Robert Lefringhausen, Annalena Daniels, Michael Fink, Sandra Hirche

School of Computation, Information and Technology, Technical University of Munich, Germany · NTFE GmbH, Munich, Germany

Optimal Control Safety-Critical Control Chance-Constrained Control

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As control engineering is applied to increasingly complex systems, learning-based control, which uses empirical data to reduce the reliance on first-principles models, has emerged as a promising paradigm. However, such approaches must cope with nonlinear and stochastic dynamics, the absence of state measurements, and scarce data, while ensuring safe operation under constraints, which requires rigorous uncertainty quantification. Whereas existing methods treat these challenges in isolation, this article proposes a Bayesian framework that jointly addresses them. A prior in state-space form is updated from input-output data using particle Markov chain Monte Carlo methods, yielding a representation of joint uncertainty over dynamics and latent states. Building on this representation, a validation procedure is developed that provides probabilistic guarantees for constraint satisfaction under arbitrary causal control laws. For control synthesis, a formulation penalizing the conditional value-at-risk of the constraint function is proposed, together with an adaptive calibration mechanism that adjusts the performance-robustness trade-off to achieve a desired robustness level. The probability of successful certification is characterized theoretically. The framework is extended to a receding-horizon setting, where posterior updates from new measurements allow the controller to progressively reduce uncertainty and improve performance. The approach is demonstrated on the optimal control of a vertical farm.