This chapter proposes a control design framework that enables to derive uncertainty-aware dynamic output feedback from the solution of a cloud of deterministic optimal control problems (OCPs)Optimal control problem. The framework assumes that the system’s equations are known, while the true values of the parameters involved in the models are quite badly known. In this context, the heuristic can be viewed as a scalable way to approximately solve stochastic Model Predictive Control (MPC) problems as formulated in Chapter The design methodology discussed in this chapter is very general and scalable in the sense that it can be applied to dynamical systems with high number of states and control inputs. However, it is rather dedicated to economic-like control problems that show no critical stabilityStability or high precision issue (This is the case of the majority, if not all, of data-driven solutions!). To rephrase it in a more evocative way, the solution is worth to be used in problems such thatCancer cancer treatment, maximizing production or minimizing energy consumption rather than in the stabilization of multiple inverted pendulums on a cart. The good news is that the latter problems generally admit more efficient standard control solutions via problem-dependent handling, generally involving the use of extended state/parameter reconstruction together with some high-gain appropriate feedback strategies. The proposed framework is illustrated via the problem of maximizing the outcome of a parallel reactor, while in chapter, it is shown how it can be used to solve the problem of power management in a hybrid vehicle using real-life data.