CONTROLLER design is more troublesome in aerospace systems due to, inter alia, diversity
of mission platforms, convoluted nonlinear dynamics, predominantly strict mission and …

Over the past decades, the advantages of optimization-based control techniques over
conventional controllers inspired developments that enabled the use of model predictive …

This paper presents the acados software package, a collection of solvers for fast embedded
optimization intended for fast embedded applications. Its interfaces to higher-level …

We show that artificial neural networks with rectifier units as activation functions can exactly
represent the piecewise affine function that results from the formulation of model predictive …

Abstract Model predictive control (MPC) is widely used in the motion control of autonomous
vehicles. However, the conventional MPC relies on an accurate model and cannot achieve …

For four wheel independent motor-drive electric vehicle, the vehicle longitudinal and lateral
motion can be controlled by distributing the driving and regenerative braking torques of four …

Abstract We present Optimization Engine (OpEn): an open-source code generation
framework for real-time embedded nonconvex optimization, which implements a novel …

Model predictive control (MPC) is an optimization-based strategy for high-performance
control that is attracting increasing interest. While MPC requires the online solution of an …

In this letter, we present qpSWIFT, a real-time quadratic program (QP) solver. Motivated by
the need for a robust embedded QP solver in robotic applications, qpSWIFT employs …

In this paper we present acados, a new software package for model predictive control. It
provides a collection of embedded optimization algorithms written in C, with a strong focus …