Accelerated gradient methods and dual decomposition in distributed model predictive control
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TLDR
The evaluation shows that the proposed distributed optimization algorithm for mixed L"1/L"2-norm optimization based on accelerated gradient methods using dual decomposition can outperform current state-of-the-art optimization software CPLEX and MOSEK.About:
This article is published in Automatica.The article was published on 2013-03-01 and is currently open access. It has received 265 citations till now. The article focuses on the topics: Optimization problem & Duality (optimization).read more
Citations
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Proceedings ArticleDOI
Structural Data Compression for Embedded Long Prediction Horizon Model Predictive Control on Resource-Constrained FPGA Platforms
TL;DR: Structural data compression (SDC), a technique that drastically lowers the memory demand of embedded MPC on FPGAs, is proposed and shown how SDC can reduce the memory requirements by a factor higher than 80x compared to common code-generated MPC designs, thereby enabling MPC systems that require long prediction horizons to be implemented on an embedded device.
Posted Content
An Accelerated Proximal Gradient-based Robust Model Predictive Control Algorithm.
TL;DR: In this article, an accelerated robust model predictive control (RMPC) algorithm for linear systems with additive disturbance is proposed based on the tube technique and the proximal gradient method, which can accelerate the iteration convergence rate.
Proceedings ArticleDOI
Dynamic Reduction of The Iterations Requirement in A Distributed Model Predictive Control
TL;DR: This paper proposes to reduce the number of iterations required in the implementation of distributed Model Predictive Control based on dual decomposition by continually fixing the value of Lagrange multipliers, and a local optimization problems dynamic sizing algorithm (LOPDSA) is proposed by continually reducing the size of local optimization problem during the iteration through an original prediction horizon reduction.
References
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