In this review paper, the integration of Machine Learning (ML) and Model Predictive Control
(MPC) in Automotive Control System (ACS) applications are discussed. ACS can be divided …

This article presents an offline solution and online approximation to the hybrid control
problem of planar non-prehensile manipulation. Hybrid dynamics and underactuation are …

Many robotics problems, from robot motion planning to object manipulation, can be modeled
as mixed-integer convex program (MICPs). However, state-of-the-art algorithms are still …

The high computational requirements of nonlinear model predictive control (NMPC) are a
long-standing issue and, among other methods, learning the control policy with machine …

Mixed-integer convex programming (MICP) has seen significant algorithmic and hardware
improvements with several orders of magnitude solve time speedups compared to 25 years …

The operation of multi-energy systems has to be optimized repeatedly, eg, to react to
changing energy prices. Thus, operational optimization problems need to be solved in a …

This paper presents a deep learning-based model predictive control (MPC) method for
operational supply chain optimization in real time. The method follows an offline-online …

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 …

Security-constrained unit commitment (SCUC) is the basis for power systems and markets
operation, which is solved periodically via mixed-integer programming (MIP) with limited …

While mixed-integer convex programs (MICPs) arise frequently in mixed-integer optimal
control problems (MIOCPs), current state-of-the-art MICP solvers are often too slow for real …