scholarly journals Stochastic Model Predictive Control for Linear Systems Using Probabilistic Reachable Sets

2018 ◽  
Author(s):  
Lukas Hewing ◽  
Melanie N. Zeilinger
Keyword(s):  
Model Predictive Control ◽  
Stochastic Model ◽  
Linear Systems ◽  
Predictive Control ◽  
Reachable Sets ◽  
Stochastic Model Predictive Control

Stochastic model predictive control for tracking linear systems

2019 ◽  
Vol 41 (1) ◽  
pp. 65-83 ◽  
Author(s):  
Agustina D' Jorge ◽  
Bruno F. Santoro ◽  
Alejandro Anderson ◽  
Alejandro H. González ◽  
Antonio Ferramosca
Keyword(s):  
Model Predictive Control ◽  
Stochastic Model ◽  
Linear Systems ◽  
Predictive Control ◽  
Stochastic Model Predictive Control

scholarly journals A Comparative Study of Stochastic Model Predictive Controllers

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2078
Author(s):  
Edwin González ◽  
Javier Sanchis ◽  
Sergio García-Nieto ◽  
José Salcedo
Keyword(s):  
Model Predictive Control ◽  
Stochastic Model ◽  
Comparative Study ◽  
Linear Systems ◽  
Predictive Control ◽  
Probabilistic Constraints ◽  
Numerical Examples ◽  
Model Predictive Controllers ◽  
Predictive Controllers ◽  
Stochastic Model Predictive Control

A comparative study of two state-of-the-art stochastic model predictive controllers for linear systems with parametric and additive uncertainties is presented. On the one hand, Stochastic Model Predictive Control (SMPC) is based on analytical methods and solves an optimal control problem (OCP) similar to a classic Model Predictive Control (MPC) with constraints. SMPC defines probabilistic constraints on the states, which are transformed into equivalent deterministic ones. On the other hand, Scenario-based Model Predictive Control (SCMPC) solves an OCP for a specified number of random realizations of uncertainties, also called scenarios. In this paper, Classic MPC, SMPC and SCMPC are compared through two numerical examples. Thanks to several Monte-Carlo simulations, performances of classic MPC, SMPC and SCMPC are compared using several criteria, such as number of successful runs, number of times the constraints are violated, integral absolute error and computational cost. Moreover, a Stochastic Model Predictive Control Toolbox was developed by the authors, available on MATLAB Central, in which it is possible to simulate a SMPC or a SCMPC to control multivariable linear systems with additive disturbances. This software was used to carry out part of the simulations of the numerical examples in this article and it can be used for results reproduction.

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