Disturbance decoupling of Boolean networks via robust indistinguishability method

Rong Zhao, Jun-e Feng, Biao Wang and Renato De Leone

Applied Mathematics and Computation, 2023, vol. 457, issue C

Abstract: Disturbances are ubiquitous and affect the normal operation of systems. This paper investigates the disturbance decoupling problem of Boolean networks (BNs) and Boolean control networks (BCNs) by a robust indistinguishability method. Utilizing a new method based on the reduced state transition matrix, the relationship between three types of disturbance decoupling and robust indistinguishability is revealed, which also builds a link between robust observability and disturbance decoupling. Based on a parameter extraction mapping, several feasible criteria are presented for original and weak disturbance decoupling of BNs and BCNs. Additionally, our approach is more concise and has a lower computational complexity than the existing methods. Finally, two examples are presented to illustrate the effectiveness of the theoretical results.

Keywords: Boolean networks; Disturbance decoupling; Robust indistinguishability; Reduced state transition matrix; Semi-tensor product (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:457:y:2023:i:c:s0096300323003892

DOI: 10.1016/j.amc.2023.128220

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