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

被引:160
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
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
收藏
页码:378 / 387
页数:10
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