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...

We consider the problem of selecting one model from a large class of plausible models. A predictive Bayesian viewpoint is advocated to avoid the speci cation of prior probabilities for the candidate models and the detailed interpretation of the parameters in each model. Using criteria derived from a certain predictive density and a prior speci cation that emphasizes the observables, we implemen...

Most academic control schemes for MIMO systems assume all the control variables are updated simultaneously. MPC outperforms other control strategies through its ability to deal with constraints. This requires on-line optimization, hence computational complexity can become an issue when applying MPC to complex systems with fast response times. The multiplexed MPC scheme described in this paper s...

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.

Nowadays, extensive research has been conducted for gas turbine engines control due to growing importance of gas turbine engines for different industries and the need to design a suitable control system for a gas turbine as the heart of the industry. In order to design gas turbine control system, various control variables can be used, but in the meantime, fuel flow inserting into combustion cha...

Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be imple...

We introduce a markup language based upon XML for working with the predictive models produced by data mining systems. The language is called the Predictive Model Markup Language (PMML) and can be used to define predictive models and ensembles of predictive models. It provides a flexible mechanism for defining schema for predictive models and supports model selection and model averaging involvin...