Proceedings Article10.1109/ECC.2016.7810473
Python code generation for explicit MPC in MPT
Balint Takacs,Juraj Stevek,Richard Valo,Michal Kvasnica +3 more
- 01 Jun 2016
pp 1328-1333
11
TL;DR: To enable implementation of discontinuous feedback laws, the paper proposes an extended version of the sequential search algorithm which resolves possible multiplicities based on a secondary evaluation of the cost function.
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Abstract: The paper shows how explicit representations of model predictive control (MPC) feedback laws can be embedded into Python applications via a new code-generation module of the Multi-Parametric Toolbox. The advantage of the explicit approach is that it provides a simple and fast computation of optimal control inputs without solving optimization problems on-line. To enable implementation of discontinuous feedback laws, the paper proposes an extended version of the sequential search algorithm which resolves possible multiplicities based on a secondary evaluation of the cost function. Two applications are considered. The first one is the Flappy Bird game where we design an MPC-based artificial player to control flapping of the bird's wings. The second application considers the design and implementation of an explicit MPC controller for a quadrocopter.
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Citations
Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward
Iosif Pappas,Dustin Kenefake,Baris Burnak,Styliani Avraamidou,Hari S. Ganesh,Justin Katz,Nikolaos A. Diangelakis,Efstratios N. Pistikopoulos +7 more
- 21 Jan 2021
TL;DR: In this article, a review article covers recent theoretical, algorithmic, and application developments in multiparametric programming, highlighting the benefits of multiparametric programs in future research efforts.
Explicit Model Predictive Control of a Magnetic Flexible Endoscope
Bruno Scaglioni,Luca Previtera,James W. Martin,Joseph C. Norton,Keith L. Obstein,Pietro Valdastri +5 more
- 16 Jan 2019
TL;DR: The work presented here constitutes an initial exploration for model-based control techniques applied to magnetically manipulated payloads; the techniques described here may be applied to a wide range of devices, including flexible endoscopes and wireless capsules.
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•Posted Content
Differentiable Predictive Control: An MPC Alternative for Unknown Nonlinear Systems using Constrained Deep Learning.
Jan Drgona,Karol Kis,Aaron Tuor,Draguna Vrabie,Martin Klaučo +4 more
- 07 Nov 2020
TL;DR: Beyond improved control performance, the DPC method scales linearly compared to exponential scalability of the explicit MPC solved via multiparametric programming, hence, opening doors for applications in nonlinear systems with a large number of variables and fast sampling rates which are beyond the reach of classical explicitMPC.
17
K-d tree based approach for point location problem in explicit model predictive control
Ju Zhang,Xiaojie Xiu,Xiaojie Xiu +2 more
TL;DR: An approach of constructing a hybrid data structure called constructed k-d tree (CKDT), which combines the k-dimensional tree (k-D tree) with the binary search tree (BST) for point location in such polyhedral sets, and involves a trade-off between memory storage requirement and online efficiency.
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A Minimal Training Strategy to Play Flappy Bird Indefinitely with NEAT
Matheus G. Cordeiro,Paulo Bruno de Sousa Serafim,Yuri Lenon Barbosa Nogueira,Creto Augusto Vidal,Joaquim Bento Cavalcante Neto +4 more
- 01 Oct 2019
TL;DR: A minimal training strategy to develop autonomous virtual players using the NEAT neuroevolutionary algorithm to evolve an agent capable of playing the Flappy Bird game is proposed.
7
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