  This paper presents a scheme for dual robust control of batch processes under
parametric uncertainty. The dual-control paradigm arises in the context of
adaptive control. A trade-off should be decided between the control actions
that (robustly) optimize the plant performance and between those that excite
the plant such that unknown plant model parameters can be learned precisely
enough to increase the robust performance of the plant. Some recently proposed
approaches can be used to tackle this problem, however, this will be done at
the price of conservativeness or significant computational burden. In order to
increase computational efficiency, we propose a scheme that uses parameterized
conditions of optimality in the adaptive predictive-control fashion. The dual
features of the controller are incorporated through scenario-based
(multi-stage) approach, which allows for modeling of the adaptive robust
decision problem and for projecting this decision into predictions of the
controller. The proposed approach is illustrated on a case study from batch
membrane filtration.
