Abstract Mixed integer Model Predictive Control (MPC) problems arise in the operation of
systems where discrete and continuous decisions must be taken simultaneously to …

Mixed‐integer model predictive control (MI‐MPC) can be a powerful tool for controlling
hybrid systems. In case of a linear‐quadratic objective in combination with linear or …

We propose a supervised learning framework for computing solutions of multi-parametric
Mixed Integer Linear Programs (MILPs) that arise in Model Predictive Control. Our approach …

This work proposes an approach that integrates reinforcement learning and model
predictive control (MPC) to efficiently solve finite-horizon optimal control problems in mixed …

Mixed-integer quadratic programs (MIQPs) are a versatile way of formulating vehicle
decision making (DM) and motion planning problems, where the prediction model is a …

Process control and optimization have been widely used to solve decision-making problems
in chemical engineering applications. However, identifying and tuning the best solution …

Future spacecraft and surface robotic missions require increasingly capable autonomy
stacks for exploring challenging and unstructured domains and trajectory optimization will …

This work introduces Transformer-based Successive Convexification (T-SCvx), an extension
of Transformer-based Powered Descent Guidance (T-PDG), generalizable for efficient six …

In this paper, we consider the problem of minimum-time optimal control for a dynamical
system with initial state uncertainties and propose a sequential convex programming (SCP) …

Machine learning has demonstrated remarkable promise for solving the trajectory
generation problem and in paving the way for online use of trajectory optimization for …