A Neurodynamic Optimization Approach to Robust Pole Assignment Based on Convex Reformulation

被引：0

作者：

Le, Xinyi ^{[1
]}

Wang, Jun ^{[1
]}

机构：

[1] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Shatin, Hong Kong, Peoples R China

来源：

2014 IEEE CONFERENCE ON CONTROL APPLICATIONS (CCA) | 2014年

关键词：

RECURRENT NEURAL-NETWORK; FEEDBACK-CONTROL-SYSTEMS; LINEAR STATE-FEEDBACK; VARIATIONAL-INEQUALITIES; PSEUDOCONVEX OPTIMIZATION; NONLINEAR OPTIMIZATION; BOUND CONSTRAINTS; SUBJECT; DESIGN; COMPUTATION;

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：

Another neurodynamic optimization approach to robust pole assignment is presented for synthesizing linear control systems. The original pseudoconvex optimization problem for robust pole assignment is reformulated as a convex optimization problem. Three coupled recurrent neural networks operating in three different time scales are developed for solving the reformulated problem in real time. It is shown that robust parametric configuration and exact pole assignment of feedback control systems can be achieved. Two examples of the proposed approach are discussed in detail to demonstrate its effectiveness.

引用

页码：1425 / 1430

页数：6

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