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In this paper, a simple heuristic is proposed for the design of uncertaintyaware predictive controllers for nonlinear models involving uncertainparameters. The method relies on Machine Learning-based approximation of idealdeterministic MPC solutions with perfectly known parameters. An efficientconstruction of the learning data set from these off-line solutions is proposedin which each solution provides many samples in the learning data. This enablesa drastic reduction of the required number of Non Linear Programming problemsto be solved off-line while explicitly exploiting the statistics of theparameters dispersion. The learning data is then used to design a fast on-lineoutput dynamic feedback that explicitly incorporate information of thestatistics of the parameters dispersion. An example is provided to illustratethe efficiency and the relevance of the proposed framework. It is in particularshown that the proposed solution recovers up to 78\% of the expected advantageof having a perfect knowledge of the parameters compared to nominal design.

A Heuristic for Dynamic Output Predictive Control Design for Uncertain Nonlinear Systems