This paper presents a technique to design switching gainscheduling controllers for plants with time-varying parameters. Given a desired L2-gain, the proposed method divides the parameter set into several subsets, and designs a robust controller for each subset. The method can achieve higher performances than traditional gain-scheduling controller design methods. A numerical example shows the ef...

Successful forecasting models strike a balance between parsimony and flexibility. This is often achieved by employing suitable shrinkage priors that penalize model complexity but also reward fit. In this article, we modify the stochastic volatility in mean (SVM) introducing state-of-the-art techniques allow for time variation degree of shrinkage. Using real-time inflation forecast exercise, sho...

Model Predictive Control (MPC) is able to directly deal with dead-time (DT) phenomena. Yet, implicit delay compensation heavily affects computational aspects of these algorithms, and stability feasibility analyses become numerically tougher. The Linear Parameter Varying (LPV) framework has been shown suitable model complex, nonlinear dynamics, corresponding MPC algorithms being developed over t...

A suitable design of state estimators requires a representative model for capturing the plant behavior and knowledge about the noise statistics, which are generally not known in practical applications. While the measurement noise covariance can be directly derived from the measurement device reproducibility, the choice of the process noise covariance is much less straightforward. Further, proce...