Stochastic Model Predictive Control Toolbox

Stochastic model predictive control (chance-constrained and scenario based) simulator for linear systems with additive disturbances.

Most stochastic MPC's can be classified within two groups: In the first group are those based in analytical methods (Chance-Constrained), which solve an OCP based on the expected value of an index cost, subject to probabilistic constraints, generally in the predicted states. In the second group are those based on random scenarios (Scenario-Based), which solve an OCP for a determined number of random realizations of uncertainties also called scenarios.

The files contain a basic Stochastic predictive control simulators for multivariable linear systems with additive disturbances. The disturbances have a Gaussian probability distribution and can be bounded. In total there are two simulators: a simulator for an MPC based on chance constraints for states; and another based on scenarios of realizations of the disturbances. In addition, for each controller there are files with examples based on a two-mass spring system implementation.

*****************************************************************

Please, before starting to use it, read the file "readme.txt"

*****************************************************************

Author: Edwin Alonso González Querubín

https://www.researchgate.net/profile/Edwin_Gonzalez_Querubin

https://es.mathworks.com/matlabcentral/profile/authors/15149689

Research Group: Predictive Control and Heuristic Optimization (CPOH)

http://cpoh.upv.es

Unversity: Universidad Politécnica de Valencia

http://www.upv.es

For more details about the stochastic predictive control algorithms implemented here, please consult the article associated with this toolbox:

https://www.mdpi.com/2079-9292/9/12/2078