Update README.md

README.md

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 ## Description
 Model predictive control (MPC) is a popular control method, which is however reliant on an accurate dynamic model. Many dynamic systems however are affected by significant uncertainties often leading to a lower performance and significant constraint violations. In this algorithm we assume that a nonlinear system is affected by known stochastic parametric uncertainties leading to a stochastic nonlinear MPC (SNMPC) approach. The square-root Unscented Kalman filter (UKF) equations are used in this context for both estimation and propagation of mean and covariance of the states by generating separate scenarios as shown in the figure above. The uncertainty description is used to optimize an objective in expectation and employ chance-constraints to maintain feasibility despite the presence of the stochastic uncertainties. The covariance of the nonlinear constraints is found using linearization. The dynamic equation system is assumed to be given by differential algebraic equations (DAE). Further description on the theory can be found in [[1]](#1)[[2]](#2). 
 
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 ## Features & benefits
 * Cheap SNMPC implementation for both receding and shrinking time horizons