Non-conservative robust Nonlinear Model Predictive Control via scenario decomposition

This work presents an optimization-based scheme for the predictive control of systems under uncertainty using multi-stage stochastic optimization and its efficient solution applying scenario decomposition techniques. The approach presented relies on the application of a robust Nonlinear Model Predictive Control (NMPC) scheme that is based on the description of the evolution of the uncertainty by a scenario tree. Since the size of the resulting optimization problem grows exponentially with the number of uncertainties taken into account and with the prediction horizon (number of stages), we discuss the use of scenario decomposition techniques as a possibility to deal with this problem. The approach is illustrated by simulation results for a nonlinear process that show that the resulting large optimization problem can be solved parallely, faster and with smaller memory requirements than using a monolithic approach.