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Machine learning-based warm starting of active set methods in embedded model predictive control
We propose to apply artificial intelligence approaches in a warm-starting procedure to
accelerate active set methods that are used to solve strictly convex quadratic programs in …
accelerate active set methods that are used to solve strictly convex quadratic programs in …
[PDF][PDF] Numerical simulation methods for embedded optimization
R Quirynen - 2017 - lirias.kuleuven.be
Dynamic optimization based control and estimation techniques have gained increasing
popularity, because of their ability to treat a wide range of problems and applications. They …
popularity, because of their ability to treat a wide range of problems and applications. They …
Towards proper assessment of QP algorithms for embedded model predictive control
With model predictive control (MPC) becoming a viable approach for advanced feedback
control at very fast sampling times, a plethora of methods for solving quadratic programming …
control at very fast sampling times, a plethora of methods for solving quadratic programming …
Exact representation and efficient approximations of linear model predictive control laws via HardTanh type deep neural networks
Deep neural networks have revolutionized many fields, including image processing, inverse
problems, text mining and more recently, give very promising results in systems and control …
problems, text mining and more recently, give very promising results in systems and control …
Algorithms and methods for high-performance model predictive control
G Frison - 2016 - orbit.dtu.dk
The goal of this thesis is to investigate algorithms and methods to reduce the solution time of
solvers for Model Predictive Control (MPC). The thesis is accompanied with an open-source …
solvers for Model Predictive Control (MPC). The thesis is accompanied with an open-source …
Newton-type alternating minimization algorithm for convex optimization
We propose a Newton-type alternating minimization algorithm (NAMA) for solving structured
nonsmooth convex optimization problems where the sum of two functions is to be minimized …
nonsmooth convex optimization problems where the sum of two functions is to be minimized …
Solving the infinite-horizon constrained LQR problem using accelerated dual proximal methods
This work presents an algorithmic scheme for solving the infinite-time constrained linear
quadratic regulation problem. We employ an accelerated version of a popular proximal …
quadratic regulation problem. We employ an accelerated version of a popular proximal …
On the convergence of inexact projection primal first-order methods for convex minimization
It is well-known that primal first-order algorithms achieve sublinear (linear) convergence for
smooth convex (smooth strongly convex) constrained minimization. However, these …
smooth convex (smooth strongly convex) constrained minimization. However, these …
Deep unfolding projected first order methods-based architectures: application to linear model predictive control
Deep learning black-box neural networks have revolutionized many fields, including image
processing, inverse problems, text mining and more recently, give very promising results in …
processing, inverse problems, text mining and more recently, give very promising results in …
[PDF][PDF] Algorithms and methods for fast model predictive control
G Frison - 2015 - people.compute.dtu.dk
Algorithms and Methods for Fast Model Predictive Control Page 1 Algorithms and Methods
for Fast Model Predictive Control Gianluca Frison Technical University of Denmark …
for Fast Model Predictive Control Gianluca Frison Technical University of Denmark …