Journal ArticleDOI
Rapid development of modular and sustainable nonlinear model predictive control solutions
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TLDR
In this paper, the authors propose a modularization of the NMPC implementations that facilitates the comparison of different solutions and the transition from simulation to online application, and the proposed platform supports the multi-stage robust NMPC approach to deal with uncertainty.About:
This article is published in Control Engineering Practice.The article was published on 2017-03-01. It has received 104 citations till now. The article focuses on the topics: Robust control.read more
Citations
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Journal ArticleDOI
CasADi: a software framework for nonlinear optimization and optimal control
TL;DR: This article gives an up-to-date and accessible introduction to the CasADi framework, which has undergone numerous design improvements over the last 7 years.
Journal ArticleDOI
A deep learning-based approach to robust nonlinear model predictive control
Sergio Lucia,Benjamin Karg +1 more
TL;DR: Empirical evidence is presented which shows that the use of deep neural networks with many hidden layers as opposed to shallow networks with only one significantly improves the learning process of a robust NMPC control law.
Journal ArticleDOI
Deep Learning-Based Model Predictive Control for Resonant Power Converters
TL;DR: In this paper, the authors proposed to learn the optimal control policy defined by a complex model predictive formulation using deep neural networks so that the online use of the learned controller requires only the evaluation of a neural network.
Posted Content
Model Predictive Control for Micro Aerial Vehicles: A Survey.
TL;DR: This paper presents a review of the design and application of model predictive control strategies for Micro Aerial Vehicles and specifically multirotor configurations such as quadrotors and an overview of recent research trends on the combined application of modern deep reinforcement learning techniques and model predictive controlled vehicles is presented.
Journal ArticleDOI
Approximate Dynamic Programming Based Control of Proppant Concentration in Hydraulic Fracturing
TL;DR: An approximate dynamic programming (ADP) based approach for the closed-loop control of hydraulic fracturing to achieve the target proppant concentration at the end of pumping at a fraction of the computational cost required by MPC while handling the uncertainty in the Young’s modulus of the rock formation.
References
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Journal ArticleDOI
On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
TL;DR: A comprehensive description of the primal-dual interior-point algorithm with a filter line-search method for nonlinear programming is provided, including the feasibility restoration phase for the filter method, second-order corrections, and inertia correction of the KKT matrix.
Journal ArticleDOI
A survey of industrial model predictive control technology
S. Joe Qin,Thomas A. Badgwell +1 more
TL;DR: An overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors, is provided in this article, where a brief history of industrial MPC technology is presented first, followed by results of our vendor survey of MPC control and identification technology.
Journal ArticleDOI
SUNDIALS: Suite of nonlinear and differential/algebraic equation solvers
Alan C. Hindmarsh,Peter Brown,K. E. Grant,Steven L. Lee,Radu Serban,Dan E. Shumaker,Carol S. Woodward +6 more
TL;DR: The current capabilities of the codes, along with some of the algorithms and heuristics used to achieve efficiency and robustness, are described.
Posted Content
Enabling New Flexibility in the SUNDIALS Suite of Nonlinear and Differential/Algebraic Equation Solvers.
TL;DR: The SUNDIALS suite of nonlinear and DIfferential/ALgebraic equation solvers (SUNDIALs) as mentioned in this paper has been redesigned to better enable the use of application-specific and third-party algebraic solvers and data structures.
Proceedings ArticleDOI
Multi-Parametric Toolbox 3.0
TL;DR: The Multi-Parametric Toolbox is a collection of algorithms for modeling, control, analysis, and deployment of constrained optimal controllers developed under Matlab that features a powerful geometric library that extends the application of the toolbox beyond optimal control to various problems arising in computational geometry.