Robust Controllability of Boolean Control Networks via Dynamic Programming

This article presents a novel dynamic programming approach to determine the robust controllability of Boolean control networks (BCNs) subject to stochastic disturbances. By applying Bellman's optimality principle, we derive the recurrence relation for computing the optimal time matrix, a crucia...

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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 36; číslo 9; s. 17448 - 17461
Hlavní autoři: Li, Yakun, Gao, Shuhua, Gao, Yiming, Wu, Jianliang, Feng, Jun-e, Xiang, Cheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.09.2025
Témata:
Boolean control networks (BCNs)
Computational efficiency
Computational modeling
Controllability
Dynamic programming
Heuristic algorithms
Perturbation methods
robust controllability
State feedback
Sufficient conditions
Time complexity
Vectors
ISSN:2162-237X, 2162-2388, 2162-2388
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Shrnutí:This article presents a novel dynamic programming approach to determine the robust controllability of Boolean control networks (BCNs) subject to stochastic disturbances. By applying Bellman's optimality principle, we derive the recurrence relation for computing the optimal time matrix, a crucial concept characterizing robust reachability between two arbitrary states. We develop a finite-termination dynamic programming algorithm to calculate the optimal time matrix exactly and efficiently, with a rigorously certified iteration count. Sufficient and necessary conditions for robust controllability are then established based on the optimal time matrix. Furthermore, for any pair of reachable states, we construct time-optimal state feedback control laws to steer the system from the initial state to the target state, regardless of disturbances. Finally, extensive numerical experiments with biological networks validate the effectiveness of the proposed approach, showing significant improvements in computational efficiency. Additionally, we introduce a Q -learning-based algorithm and compare its performance, highlighting the advantages of our dynamic programming approach in terms of both efficiency and solution quality.
Bibliografie:ObjectType-Article-1
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2025.3559207