Robust Model Predictive Control
Model-predictive control (MPC) of dynamic systems has become a standard
control strategy especially for multivariate systems with constraints.
In MPC, a mathematical model of a dynamic system is used to predict the
behavior of the system and a sequence of optimal control inputs is
obtained by minimizing a pre-defined cost function subject to constraints.
One of the main challenges of model-based control strategies is that real
world systems are uncertain or affected by disturbances, and it is well known
that neglecting the uncertainty may significantly deteriorate the performance
and might destroy the stability properties of optimization-based control schemes.
Therefore, it is crucial to develop approaches that take the presence of
uncertainties into account. Several methods have been presented in the last
years but there still exists no approach that simultaneously is not overly
conservative, is real-time implementable, can guarantee stability and recursive
feasibility and is applicable to large scale or nonlinear systems.
This minisymposium presents some of the recent advances for dealing with
uncertainty in the framework of model predictive control.