Model Predictive Control

Also known as MPC

Model Predictive Control (MPC) predicts how a system will evolve, optimizes a sequence of actions over a finite horizon, applies the first action, and replans as measurements arrive. Work spans linear and nonlinear optimization, sampling-based methods such as MPPI, and learned dynamics from Gaussian processes, Koopman models, and neural world models; these methods are used in robotics, autonomous vehicles, energy systems, and process control. Robust and stochastic MPC, control barrier functions, reachability, and distributed optimization address uncertainty, safety, and coordination, while GPU and embedded solvers aim to meet real-time limits. A central tension is that longer, richer predictions can improve planning but increase computation and sensitivity to model error, so practical controllers must balance performance, safety guarantees, and reliable execution.

Latest papers

305 papers40 papers in the last four weeks, down 7% on the four weeks before. 0.2% of all new papers.

Differentiable Model Predictive Safety for Heterogeneous Mobility at Urban Intersections
May 19, 2026Wenzhe Song, Hao ZhangMulti-Agent Coordination Intelligent Transportation Systems
School of Business Stevens Institute of Technology Hoboken, 07030, USA · Department of Mechanical Engineering Carnegie Mellon University Pittsburgh, 15213, USA
Probabilistic Recursively Feasible Motion Planning Under Uncertain Environments
May 18, 2026Hyeontae Sung, Hyeongchan Ham, Junyoung Park +2Constrained Motion Planning Chance-Constrained Motion Planning
School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea · The SYCAMORE Lab, ´Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Switzerland
Dynamic robotic cloth folding with efficient Koopman operator-based model predictive control
May 18, 2026Edoardo Caldarelli, Franco Coltraro, Adrià Colomé +2Model Predictive Control Robotic Manipulation
Istituto Italiano di Tecnologia, Genoa, Italy · Institut de Rob`otica i Inform`atica Industrial, CSIC–UPC, Barcelona, Spain · MaLGa Center, DIB-RIS, Universit`a degli Studi di Genova, Genoa, Italy
EfficientTDMPC: Improved MPC Objectives for Sample-Efficient Continuous Control
May 15, 2026Thomas Evers, Cristian Meo, Wendelin Bohmer +2Model Predictive Control Continuous Control
TU Delft · LatentWorlds AI
Constrained MPC-Based Motion Planning for Morphing Quadrotors in Ultra-Narrow Passages under Limited Perception
May 15, 2026Harsh Modi, Xiao Liang, Minghui ZhengAerial Robotics Constrained Motion Planning
Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA · Department of Civil & Environmental Engineering, Texas A&M University, College Station, TX 77843, USA
Slot-MPC: Goal-Conditioned Model Predictive Control with Object-Centric Representations
May 14, 2026Jonathan Spieler, Angel Villar-Corrales, Sven BehnkeWorld Models for Robotics World Model-Based Planning
Autonomous Intelligent Systems, Computer Science Institute VI - Intelligent Systems and Robotics, Center for Robotics and the Lamarr Institute for Machine Learning and Artificial Intelligence, University of Bonn, Germany
FU-MPC: Frontier- and Uncertainty-Aware Model Predictive Control for Efficient and Accurate UAV Exploration with Motorized LiDAR
May 14, 2026Jianping Li, Pengfei Wan, Zhongyuan Liu +6Model Predictive Control Motion Planning
TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics
May 13, 2026Ishaan Mahajan, Jon Arrizabalaga, Andrea Grillo +4Semidefinite Programming Constrained Motion Planning
Columbia University, USA · Massachusetts Institute of Technology, USA · ´Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Switzerland +1
Multistep Belief Space Dynamics Learning For Risk-Aware Control
May 12, 2026Jason Gibson, Bogdan Vlahov, Patrick Spieler +1Belief-Space Planning Autonomous Driving
Georgia Institute of Technology, Atlanta GA 30313 USA · California Institute of Technology, Pasadena, CA 91125, USA
Decentralized Contingency MPC based on Safe Sets for Nonlinear Multi-agent Collision Avoidance
May 11, 2026Max Studt, Georg SchildbachCollision Avoidance Model Predictive Control
Institute for Electrical Engineering in Medicine, University of Luebeck, Luebeck, Germany
Hierarchical Causal Abduction: A Foundation Framework for Explainable Model Predictive Control
May 11, 2026Ramesh Arvind Naagarajan, Zühal Wagner, Stefan StreifNonlinear MPC Model Predictive Control
Professorship for Automatic Control and System Dynamics, Chemnitz University of Technology, Chemnitz, Germany · Department of Bioresources, Fraunhofer Institute for Molecular Biology and Applied Ecology, Giessen, Germany.
Mismatch-Aware Adaptive Constraint Tightening for Bicycle-Model Trajectory Optimization
May 10, 2026Lingxue Lyu, Zihui LiuTrajectory Optimization Model Predictive Control
School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA · Department of Aeronautics & Astronautics, Stanford University, Stanford, CA 94305, USA
SHIELD: Scalable Optimal Control with Certification using Duality and Convexity
May 9, 2026Hansung Kim, Siddharth H. Nair, Francesco BorrelliPrimal-Dual Optimization Model Predictive Control
Model Predictive Control Laboratory, UC Berkeley · Nextracker Inc.
Sensitivity-Based Robust NMPC for Close-Proximity Offshore Wind Turbine Inspection with a Tilted Multirotor
May 8, 2026Giuseppe Silano, Martin SaskaIndustrial Inspection Robust Control
Ricerca sul Sistema Energetico S.p.A., Milan, Italy, and the Czech Technical University in Prague, Czechia · Czech Technical University in Prague, Czechia
Interactive Trajectory Planning with Learning-based Distributionally Robust Model Predictive Control and Markov Systems
May 8, 2026Erik Börve, Nikolce Murgovski, Morteza Haghir Chehreghani +1Distributionally Robust Optimization Model Predictive Control
Chalmers University of Technology, Chalmersgatan 4, 412 96 Göteborg, Sweden · Volvo Group Trucks Technology, Gropegårdsgatan 2, 417 15 Göteborg, Sweden
Accurate Trajectory Tracking with MPCC for Flapping-Wing MAVs
May 7, 2026Charbel Toumieh, Jack Zeng, Niel Mistry +1Robot Trajectory Tracking Aerial Robotics
Laboratory of Intelligent Systems, Ecole Polytechnique Federale de Lausanne (EPFL), CH1015 Lausanne, Switzerland
Maximal Controlled Invariant-MPC: Enhancing Feasibility and Reducing Conservatism through Terminal CBF Constraint in Safety-Critical Control
May 7, 2026Tanmay Dokania, Yashwanth Kumar NakkaDynamical Systems Safety-Critical Control
Graduate Student, Daniel Guggenheim School of Aerospace Engineering. · Assistant Professor, Director, Aerospace Robotics Lab, Daniel Guggenheim School of Aerospace Engineering.
Bilinear Mamba-Koopman Neural MPC for Varying Dynamics
May 6, 2026Matan Pagi, Zohar SorekOptimal Control Dynamical Systems
Psistar AI
ELVIS: Ensemble-Calibrated Latent Imagination for Long-Horizon Visual MPC
May 6, 2026Yurui Du, Pinhao Song, Yutong Hu +1Visual SSMs Model Predictive Control
KU Leuven, Dept. Electrical Engineering, Research unit Processing Speech and Images, B-3000 Leuven, Belgium · Flanders Make at KU Leuven, B-3000 Leuven, Belgium · KU Leuven, Dept. Mechanical Engineering, Research unit Robotics, Automation and Mechatronics, B-3000 Leuven, Belgium
Right Model, Right Time: Real-Time Cascaded-Fidelity MPC for Bipedal Walking
May 6, 2026Franek Stark, Felix Wiebe, Shubham Vyas +2Nonlinear MPC Whole-Body Control
Robotics Innovation Center at the German Research Center for Artificial Intelligence (DFKI), Bremen, Germany · University Bremen, Germany
Dream-MPC: Gradient-Based Model Predictive Control with Latent Imagination
May 6, 2026Jonathan Spieler, Sven BehnkeModel Predictive Control Optimal Control
Autonomous Intelligent Systems, Computer Science Institute VI - Intelligent Systems and Robotics, Center for Robotics and the Lamarr Institute for Machine Learning and Artificial Intelligence, University of Bonn, Germany.
Sensorless State Estimation and Control for Agile Cable-Suspended Payload Transport by Quadrotors
May 5, 2026Ana Maria Nascimento, Augusto Sales, Antonio Marcus Lima +1Aerial Robotics Nonlinear MPC
On Surprising Effects of Risk-Aware Domain Randomization for Contact-Rich Sampling-based Predictive Control
May 5, 2026Sergio A. Esteban, Junheng Li, Vince Kurtz +1Domain Randomization Contact-Rich Manipulation
Department of Mechanical and Civil Engineering, California Institute of Technology, Pasadena, CA, USA · School of Computing, DePaul University, Chicago, IL, USA
Robust Adaptive Predictive Control for Hook-Based Aerial Transportation Between Moving Platforms
May 4, 2026Péter Antal, Andrea Carron, Melanie Zeilinger +2Robust Control Model Predictive Control
Systems and Control Lab, HUN-REN Institute for Computer Science and Control, Budapest, Hungary · Institute for Dynamic Systems and Control, ETH Zürich, Zürich, Switzerland · Control Systems Group of the Eindhoven University of Technology, Eindhoven, The Netherlands
Sampling-Based Control via Entropy-Regularized Optimal Transport
May 4, 2026Vincent Pacelli, Akash Ratheesh, Evangelos A. TheodorouRobotic Control Nonlinear MPC
Autonomous Control and Decision Systems Laboratory Georgia Institute of Technology
Optimizing Trajectory-Trees in Belief Space: An Application from Model Predictive Control to Task and Motion Planning
May 3, 2026Camille Phiquepal, Marc ToussaintBelief-Space Planning Model Predictive Control
Learning and Intelligent Systems Lab (LIS), TU Berlin, Germany
Learning to Spend: Model Predictive Control for Budgeting under Non-Stationary Returns
Apr 29, 2026Nilavra Pathak, Smriti Shyamal, Prasant Mhasker +1Stochastic Optimal Control Model Predictive Control
Marketing Data Science, Expedia Group · Department of Chemical Engineering, McMaster University
Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics
Apr 29, 2026Bernd Frauenknecht, Lukas Kesper, Daniel Mayfrank +2Uncertainty Quantification Model Predictive Control
Institute for Data Science in Mechanical Engineering (DSME), RWTH Aachen University · Institute of Climate and Energy Systems (ICE), Energy Systems Engineering (ICE-1), Forschungszentrum Jülich GmbH
Sensitivity-Based Tube NMPC for Cooperative Aerial Structures Under Parametric Uncertainty
Apr 28, 2026Giuseppe Silano, Quentin Sablé, Marco Tognon +2Aerial Robotics Nonlinear MPC
Ricerca sul Sistema Energetico S.p.A., Milan, Italy, and Czech Technical University, Prague, Czechia · University of Twente, Enschede, The Netherlands · University of Rennes, CNRS, Inria, IRISA, 35042, France +2
Exploiting Differential Flatness for Efficient Learning-based Model Predictive Control of Constrained Multi-Input Control Affine Systems
Apr 27, 2026Tobias A. Farger, Adam W. Hall, Angela P. SchoelligNonlinear MPC Model Predictive Control
Learning Systems and Robotics Lab at the Technical University of Munich, Munich, Germany. · Learning Systems and Robotics Lab at the University of Toronto Institute for Aerospace Studies (UTIAS) and the Vector Institute for Artificial Intelligence, Toronto, Canada

May 19, 2026J/K move · Enter open · S save

Differentiable Model Predictive Safety for Heterogeneous Mobility at Urban Intersections

Wenzhe Song, Hao Zhang

School of Business Stevens Institute of Technology Hoboken, 07030, USA · Department of Mechanical Engineering Carnegie Mellon University Pittsburgh, 15213, USA

Multi-Agent Coordination Intelligent Transportation Systems Model Predictive Control Safe RL Multi-Agent Reinforcement Learning

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The imminent integration of autonomous vehicles and mobile robots in urban settings presents a critical safety challenge for future intelligent transportation systems. This paper addresses the complex problem of coordinating heterogeneous agents with disparate dynamics at unregulated intersections. We introduce a novel framework, differentiable model predictive safety (DMPS), which embeds the foresight of model-predictive control into a data-driven, end-to-end reinforcement learning architecture. DMPS agents learn a latent dynamics model to predict future trajectories contingent on their actions. A learned, differentiable safety critic then evaluates the risk of these trajectories. Crucially, by leveraging backpropagation through the entire unrolled predictive model, agents can efficiently compute the gradient of future safety with respect to their current action, enabling a minimal and precise online safety correction. Integrated into a multi-agent training scheme, DMPS virtually eliminates collisions to less than 5.6% in high-density, mixed vehicle-robot traffic simulations, demonstrating state-of-the-art safety without compromising energy and traffic efficiency.