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Regulatory networks (RNs) are a well-accepted modelling formalism in computational systems biology. The control of RNs is currently receiving a lot of attention because it provides a computational basis for cell reprogramming -- an attractive technology developed in regenerative medicine. By solving the control problem, we learn which parts of a biological system should be perturbed to stabilise the system in the desired phenotype. We allow the specification of the Boolean model representing a given RN to be incomplete. To that end, we utilise the formalism of partially specified Boolean networks which covers every possible behaviour of unspecified parts of the system. Such an approach causes a significant state explosion. This problem is addressed by using symbolic methods to represent both the unspecified model parts and all possible perturbations of the system. Additionally, to make the control design efficient and practically applicable, the optimal control should be minimal in terms of size. Moreover, in a partially specified model, a control may achieve the desired stabilisation only for a subset of the possible fully specified model instantiations. To address these aspects, we utilise several quantitative measures. Apart from the size of perturbation, we also examine its robustness -- a portion of instantiations for which the control is applicable. We show that proposed symbolic methods solving the control problem for partially specified BNs are efficient and scale well. We also evaluate the robustness metrics in cases of all three studied control types. The robustness metric tells us how big a proportion of fully defined systems the given perturbation works. Our experiments support the hypothesis that one-step perturbations may be less robust than temporary or permanent perturbations. This is a full version of a paper that is submitted to a journal.