Model Predictive Control
Model Predictive Control (MPC) is an advanced control technique that optimizes control actions over a predicted future time horizon, subject to constraints. Current research emphasizes improving MPC's computational efficiency for real-time applications, particularly in robotics, through parallel computing, accelerated gradient descent, and neural network approximations (e.g., differentiable predictive control, TransformerMPC). This focus stems from MPC's importance in diverse fields, including robotics, battery management, and autonomous driving, where its ability to handle complex systems and safety constraints is crucial.
Papers
Learning Model Predictive Control Parameters via Bayesian Optimization for Battery Fast Charging
Sebastian Hirt, Andreas Höhl, Joachim Schaeffer, Johannes Pohlodek, Richard D. Braatz, Rolf Findeisen
Robot Safe Planning In Dynamic Environments Based On Model Predictive Control Using Control Barrier Function
Zetao Lu, Kaijun Feng, Jun Xu, Haoyao Chen, Yunjiang Lou
Code Generation for Conic Model-Predictive Control on Microcontrollers with TinyMPC
Sam Schoedel, Khai Nguyen, Elakhya Nedumaran, Brian Plancher, Zachary Manchester
Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems
Ehsan Sabouni, H.M. Sabbir Ahmad, Vittorio Giammarino, Christos G. Cassandras, Ioannis Ch. Paschalidis, Wenchao Li