This paper presents an extension of the recent multi-parametric (mp-)NCO-tracking methodology by Sun et al. [Comput. Chem.
Eng. 92:64-77, 2016] for the design of robust multi-parametric controllers for constrained continuous-time linear systems in the
presence of uncertainty. We propose a robust-counterpart formulation and solution of multi-parametric dynamic optimization (mp-
DO), whereby the constraints are backed-o
ff
based on a worst-case propagation of the uncertainty using either interval analysis or
ellipsoidal calculus and an ancillary linear state feedback. We address the case of additive uncertainty, and we discuss approaches
to dealing with multiplicative uncertainty that retain tractability of the mp-NCO-tracking design problem, subject to extra conser-
vativeness. In order to assist with the implementation of these controllers, we also investigate the use of data classifiers based on
deep learning for approximating the critical regions in continuous-time mp-DO problems, and subsequently searching for a critical
region during on-line execution. We illustrate these developments with the case studies of a fluid catalytic cracking (FCC) unit and
a chemical reactor cascade.