A robust model predictive control algorithm is presented that explicitly handles multiplicative,
or parametric, uncertainty in linear discrete models over a finite horizon. The uncertainty in …

The technical note develops a receding horizon control strategy to guarantee closed-loop
convergence and feasibility in respect of soft constraints. Earlier results addressed the case …

Abstract A robust Model Predictive Control (MPC) strategy is proposed for linear systems
with multiplicative uncertainty. The uncertainty in the prediction horizon is bounded by …

Uncertainty forms an integral part of most control problems and earlier chapters discussed
how MPC algorithms can be constructed in order to treat model uncertainty in a robust …

In this work the problem of robust model predictive control (robust MPC) of a wind turbine in
the full load region is considered. A minimax robust MPC approach is used to tackle the …

This thesis introduces a new interpretation of the problems arising in robust model predictive
control (MPC). In practice, MPC algorithms are typically embedded within a multi-level …

The presence of uncertainty in model predictive control (MPC) has been accounted for using
two types of approaches: robust MPC (RMPC) and stochastic MPC (SMPC). Ideal RMPC …

This thesis presents both an applied study and a theoretical study within the field of control
theory. Control theory is an interdisciplinary branch between mathematics and engineering …

The essential components of the classical predictive control algorithms considered in Chap.
2 also underpin the design of algorithms for robust MPC. Guarantees of closed-loop …

This paper studies the output‐feedback model predictive control (MPC) design problem for
linear systems with multiplicative and additive random uncertainty. We first present an off …