Optimal neurocontrollers for discretized distributed parameter systems

被引：0
|
作者：
Prokhorov, DV [1 ]
机构：
[1] Ford Motor Co, Res & Adv Engn, Dearborn, MI 48121 USA
来源：
PROCEEDINGS OF THE 2003 AMERICAN CONTROL CONFERENCE, VOLS 1-6 | 2003年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We propose to use the framework of backpropagation through time (BPTT) [1], [2], [3] to create optimal feedback neurocontrollers for distributed parameter systems (DPS). DPS are systems distributed in space while evolving in time. Unlike the lumped parameter systems, DPS are represented by a set of partial differential equations in the state space. Our neurocontrollers obtained for discretized DPS in the infinite-horizon regulator setting are applicable to a broad set of initial states (an envelope of initial state profiles). We compare our technique and results with another approach to synthesizing optimal DPS neurocontrollers introduced in [4], [5].
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页码：549 / 554
页数：6
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