Optimal control of Boolean control networks with average cost: A policy iteration approach

被引：177

作者：

Wu, Yuhu ^{[1
]}

Sun, Xi-Ming ^{[1
]}

Zhao, Xudong ^{[1
]}

Shen, Tielong ^{[2
]}

机构：

[1] Dalian Univ Technol, Sch Control Sci & Engn, Dalian 116024, Peoples R China

[2] Sophia Univ, Dept Engn & Appl Sci, Tokyo, Japan

来源：

AUTOMATICA | 2019年 / 100卷

基金：

中国国家自然科学基金;

关键词：

Boolean control networks; Semi-tensor product (STP); Average optimal control; Infinite horizon optimal control; Policy iteration;

D O I：

10.1016/j.automatica.2018.11.036

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper deals with the infinite horizon optimal control problem for deterministic Boolean control networks (BCNs) with average cost. Based on the semi-tensor product of matrices and Jordan decomposition technique, a nested optimality equation for the average infinite horizon problem of BCNs is presented. By resorting to Laurent series expression, a novel policy iteration algorithm, which can find the optimal state feedback controller in finite iteration steps, is proposed. Finally, as a practical application, the optimal intervention problem of Ara operon in E. coil is addressed. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：378 / 387

页数：10

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