Abstract

Model predictive control (MPC) is one of the most successful advanced control technique thanks to its capability to cope with the constraints of the state and inputs and to minimize the performance of the plant.

The MPC is typically designed to regulate the plant in a certain equilibrium point or periodic trajectory where the plant is operated.

However, the operation conditions of the system might be changed, due to changes in the reference signal (in a tracking control problem) or changes in the economic cost function used to calculate the optimal operation of the plant (in an economic control problem).

In this scenario, the controller must steer the system to the new equilibrium point or periodic trajectory, which may lead to possible loss of feasibility and stability of the designed predictive controller.

This talk is devoted to present recent results on model predictive control techniques capable to cope with abrupt and random changes in the operation conditions ensuring feasibility and stability to the new operation point or trajectory.