Model predictive control (MPC) has established itself as a promising control methodology in
power electronics. This survey paper highlights the most relevant MPC techniques for power …

The inevitable presence of uncertain parameters in critical applications of process
optimization can lead to undesirable or infeasible solutions. For this reason, optimization …

The predominant paradigm for using machine learning models on a device is to train a
model in the cloud and perform inference using the trained model on the device. However …

Computing the gradient of rigid body dynamics is a central operation in many state-of-the-art
planning and control algorithms in robotics. Parallel computing platforms such as GPUs and …

Distributed model predictive control (DMPC) is a flexible and scalable feedback control
method applicable to a wide range of systems. While the stability analysis of DMPC is quite …

Applications in robotics or other size-, weight-, and power-constrained (SWaP) autonomous
systems at the edge often require real-time and low-energy solutions to large optimization …

Rigid body dynamics is a core technology in the robotics field. In trajectory optimization and
model predictive control algorithms, there are usually a large number of rigid body dynamics …

This paper presents a novel real-time implementation of linear model predictive control
(MPC) schemes based on region-less explicit MPC (RL-EMPC). RL-EMPC is a recently …

This article presents the field-programmable gate array (FPGA) implementation of a particle
swarm optimization (PSO)-based nonlinear model-predictive control (NMPC) for …

This Ph. D. dissertation contains results in two different but related fields: the implementation
of model predictive control (MPC) in embedded systems using first order methods, and …