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Titlebook: Distributed Model Predictive Control Made Easy; José M. Maestre,Rudy R. Negenborn Book 2014 Springer Science+Business Media Dordrecht 2014

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樓主: 銀河
21#
發(fā)表于 2025-3-25 06:20:14 | 只看該作者
Distributed Lyapunov-Based MPCporal monotonicity of the network-wide CLF. For infinity-norm based structured CLFs and input-affine dynamics, we show that the decentralized MPC algorithm can be implemented by solving a single linear program in each network node. Two application examples are provided to illustrate the effectivenes
22#
發(fā)表于 2025-3-25 09:37:20 | 只看該作者
Book 2014tures make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those who want to gain a deeper insight in the wide range of distributed MPC techniques available..
23#
發(fā)表于 2025-3-25 14:35:46 | 只看該作者
24#
發(fā)表于 2025-3-25 19:11:05 | 只看該作者
25#
發(fā)表于 2025-3-25 20:33:48 | 只看該作者
https://doi.org/10.1057/9780230270855poral monotonicity of the network-wide CLF. For infinity-norm based structured CLFs and input-affine dynamics, we show that the decentralized MPC algorithm can be implemented by solving a single linear program in each network node. Two application examples are provided to illustrate the effectivenes
26#
發(fā)表于 2025-3-26 02:41:48 | 只看該作者
2213-8986 buted MPC as well as for those who want to gain a deeper insight in the wide range of distributed MPC techniques available..978-94-024-0714-3978-94-007-7006-5Series ISSN 2213-8986 Series E-ISSN 2213-8994
27#
發(fā)表于 2025-3-26 07:47:42 | 只看該作者
https://doi.org/10.1057/9780230252998tual benefit. This allows to deal with the cooperation issues of the DMPC problem. Additionally, the bargaining game framework allows to formulate solutions where the subsystems do not have to solve more than one optimization at each time step. This also reduces the computational burden of the local optimization problems.
28#
發(fā)表于 2025-3-26 09:40:40 | 只看該作者
29#
發(fā)表于 2025-3-26 14:27:09 | 只看該作者
30#
發(fā)表于 2025-3-26 19:41:25 | 只看該作者
Rate Analysis of Inexact Dual Fast Gradient Method for Distributed MPCain a distributed control strategy that has the following features: state and input constraints are satisfied, stability of the plant is guaranteed, whilst the number of iterations for the suboptimal solution can be precisely determined.
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