Distributed Model Predictive Control with Event-Based Optimization <ce:cross-ref refid='fn1'> <ce:sup loc='post'>*</ce:sup> </ce:cross-ref> <ce:footnote id='fn1'> <ce:label>*</ce:label> <ce:note-para id='npara0001' view='all'>This work is supported by the German Research Foundation (DFG) under grant GO 2622/1-1</ce:note-para> </ce:footnote>

Ramin Rostami; Daniel G?rges

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This work is supported by the German Research Foundation (DFG) under grant GO 2622/1-1

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Distributed Model Predictive Control with Event-Based Optimization * * This work is supported by the German Research Foundation (DFG) under grant GO 2622/1-1

机译：基于事件的优化的分布式模型预测控制 * * 这项研究得到德国研究基金会（DFG）的GO资助2622 / 1-1

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Distributed model predictive control (DMPC) methods that are based on iterative optimization algorithms normally require a large number of communications between the controllers, especially when the number of subsystems is large. This can easily result in overloading the network which is normally shared between many different users. Therefore methods to reduce the load on the network while still satisfying convergence, feasibility, stability, and a certain level of performance are of a great importance. In this paper a novel event-based optimization algorithm is provided to reduce the number of communications in DMPC methods that are based on dual decomposition.

机译：基于迭代优化算法的分布式模型预测控制（DMPC）方法通常需要控制器之间进行大量通信，尤其是在子系统数量较大时。这很容易导致网络过载，通常这是许多不同用户之间共享的。因此，在仍然满足收敛性，可行性，稳定性和一定水平的性能的同时减少网络负载的方法非常重要。在本文中，提供了一种新颖的基于事件的优化算法，以减少基于对偶分解的DMPC方法中的通信数量。

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《IFAC PapersOnLine》 |2017年第1期|共6页
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Ramin Rostami; Daniel G?rges;
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中图分类自动化技术、计算机技术;
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Distributed Model Predictive Control with Event-Based Optimization * * This work is supported by the German Research Foundation (DFG) under grant GO 2622/1-1

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