MBPC is a feedback-control methodology suitable to enforce efficiently hard constraints on the variables of the controlled system. It is shown that the method hinges upon a constrained open-loop optimal control problem along with the adoption of the so-called receding-horizon control strategy. In the important case of time-invariant linear saturated ANCBI systems, MBPC algorithms can be devised...

Due to simplicity and convenience, Model Predictive Control, which consists in optimizing future decisions based on a pessimistic deterministic forecast of the random processes, is one of the main tools for stochastic control. Yet, it suffers from a large computation time, unless the tactical horizon (i.e. the number of future time steps included in the optimization) is strongly reduced, and la...

The robust control problem concerns to the control of plants that are only approximately known. Usually, it is assumed that the plant lies in a set of possible plants and this set can be quantitatively characterized. It is sought a control design that assures some kind of performance, which includes stability, for all the members of the family of candidate plants. Robust control theory usually ...

Model Predictive Control (MPC) is a control strategy that has been used successfully in numerous and diverse application areas. The aim of the present article is to discuss how the basic ideas of MPC can be extended to problems involving random model uncertainty with known probability distribution. We discuss cost indices, constraints, closed loop properties and implementation issues.

This paper introduces a new approach to reduce the computational load of nonlinear model based predictive controllers. The idea is based on dividing a long prediction horizon into only a few equidistant intervals with piecewise constant control signals. After solving a first dynamic optimization problem the prediction horizon is halved, keeping the second half of the solution fixed and doubling...

model predictive controller is widely used in industrial plants. uncertainty is one of the critical issues in real systems. in this paper, the direct adaptive simplified model predictive control (smpc) is proposed for unknown or time varying plants with uncertainties. by estimating the plant step response in each sample, the controller is designed and the controller coefficients are directly ca...