In this thesis a nonlinear model predictive control (NMPC) scheme is presented that is based on the concept of passivity. The proposed NMPC scheme takes advantage of the relationship between optimal control and passivity and the relationship between optimal control and model predictive control. This novel NMPC scheme, guaranteeing closedloop stability through the incorporation of a passivity-ba...

Nonlinear Model Predictive Control (NMPC) is a powerful control framework, which strongly relies on a good model of the system dynamics. In the case, such a model is not available apriori, non-parametric regression using Bayesian regression or Gaussian Processes (GPs) have been shown promising in inferring the dynamics from collected data. An advantage of Bayesian methods and GPs over other reg...

Model Predictive Control (MPC) has become one of the most popular control techniques in the process industry mainly because of its ability to deal with multiple-input-multipleoutput plants and with constraints. However, its performance can deteriorate in the presence of model uncertainties and disturbances. In the last years, the development of robust MPC techniques has been widely discussed, b...

A successful method for model predictive control of constrained linear systems uses a local linear control law that, in the presence of disturbances, maintains the disturbed trajectory of the controlled system in a tube whose centre is the nominal trajectory (generated ignoring the disturbance) and whose ‘cross-section’ is a robust, positively invariant set; robust exponential stability of an i...

In this article, we describe the design and implementation of a current controller for reluctance synchronous machine (RSM) based on continuous control set nonlinear model predictive (NMPC). A computationally efficient gray box flux linkage map, Gaussian-linear-arctangent (GLA) model, is proposed employed in tracking formulation, which implemented using high-performance framework NMPC <monospac...