In the present times, Model Predictive Control (MPC) is the standard for constrained control. This scheme computes the prediction
of the control action over a sufficiently long horizon by solving an optimization problem online. Its strong point is also its
weakest: to have the certainty that the problem will be solved on time, the system MPC is applied to must be slow enough –
especially when dealing with nonlinear systems.

The goal of this project is to develop and extend constrained control schemes that do
not rely on optimization in the loop. In this project, we focus in the recently proposed Explicit Reference Governor. In essence,
this scheme modifies the applied reference to the system in a way that makes the controlled variables fulfill the constraints. All
of this is done by computing a certain safety index, the Dynamic Safety Margin. Up to now, computing a suitable Dynamic Safety
Margin is not straightforward, except for linear systems subject to linear constraints. This project aims to extend the ERG with new
ways of computing the Dynamic Safety Margin for wider ranges of constraints and with definitions of the Dynamic Safety
Margin.

For more information, visit the website of the project:

https://saasofcc.wordpress.com