Predictive Control
Model Predictive Control (MPC) is an advanced control technique that optimizes a system's trajectory over a future time horizon by iteratively solving optimization problems. Current research emphasizes improving MPC's efficiency and robustness, particularly through integrating machine learning methods like reinforcement learning and neural networks (e.g., using transformers for faster computation or neural networks to approximate MPC solutions), and addressing challenges posed by model uncertainties and safety constraints using techniques such as control barrier functions and Bayesian optimization. These advancements are significantly impacting various fields, including robotics, autonomous vehicles, and energy systems, by enabling more efficient, safe, and adaptable control of complex systems.
Papers
Control-Tree Optimization: an approach to MPC under discrete Partial Observability
Camille Phiquepal, Marc Toussaint
A Kubernetes-Based Edge Architecture for Controlling the Trajectory of a Resource-Constrained Aerial Robot by Enabling Model Predictive Control
Achilleas Santi Seisa, Sumeet Gajanan Satpute, George Nikolakopoulos
OpTaS: An Optimization-based Task Specification Library for Trajectory Optimization and Model Predictive Control
Christopher E. Mower, João Moura, Nazanin Zamani Behabadi, Sethu Vijayakumar, Tom Vercauteren, Christos Bergeles
Benchmarking Model Predictive Control Algorithms in Building Optimization Testing Framework (BOPTEST)
Saman Mostafavi, Chihyeon Song, Aayushman Sharma, Raman Goyal, Alejandro Brito
Incorporating Recurrent Reinforcement Learning into Model Predictive Control for Adaptive Control in Autonomous Driving
Yuan Zhang, Joschka Boedecker, Chuxuan Li, Guyue Zhou
PACED-5G: Predictive Autonomous Control using Edge for Drones over 5G
Viswa Narayanan Sankaranarayanan, Gerasimos Damigos, Achilleas Santi Seisa, Sumeet Gajanan Satpute, Tore Lindgren, George Nikolakopoulos