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

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 pr...

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Bibliographic Details
Published in:Automatica (Oxford) 2019-02, Vol.100, p.378-387
Main Authors: Wu, Yuhu, Sun, Xi-Ming, Zhao, Xudong, Shen, Tielong
Format: Article
Language:English
Subjects:
Average optimal control
Boolean control networks
Infinite horizon optimal control
Policy iteration
Semi-tensor product (STP)
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Summary: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.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2018.11.036