Stochastic Model Predictive Control
Many applications of optimization and control are performed in a deterministic setting. That is, the quantities of the problem such as the state variables, control variables, and or parameters of a model are treated as fixed known values. This assumption may be reasonable in applications such as robotics, but in others such as resource allocation, we may need to incorporate the uncertainty of various quantities in order to compute the expectation of the objective. ...