A data-driven framework for constrained control of Boolean networks

In this paper, a new data-driven framework is proposed for controller design of Boolean networks, without requiring prior knowledge of the network structure. A key advantage is its capability to handle various state, input, or performance constraints that commonly arise in practical control tasks while ensuring stabilization. Unlike linear systems, generating sufficient data for Boolean networks presents unique challenges. To address this, an implementable method is developed to construct suitable input sequences that guarantee data richness for controller design. The advantage and applicability of our framework are demonstrated through two representative scenarios, namely stabilization under temporal tasks and stabilization with optimal control, for illustration. Finally, the theoretical results are validated through a biological example.