The growing interest in model predictive control for nonlinear systems, also called NMPC, is motivated by the fact that today’s processes need to be operated under tighter performance specifications to guarantee profitable and environmentally safe production. One of the remaining essential problems for NMPC is the high on-line computational load. At each sampling instant, a nonlinear optimal co...

Therefore, the presence of the plant model is a necessary condition for the development of the predictive control. The success of MPC depends on the degree of precision of the plant model. In practice, modelling real plants inherently includes uncertainties that have to be considered in control design, that is control design procedure has to guarantee robustness properties such as stability and...

This paper proposes a novel control scheme, named self-reflective model predictive control, which takes its own limitations in the presence of process noise and measurement errors into account. In contrast to existing output-feedback MPC and persistently exciting MPC controllers, the proposed self-reflective MPC controller does not only propagate a matrix-valued state forward in time in order t...

This thesis extends Model Predictive Control (MPC) for constrained linear systems subject to uncertainty, including persistent disturbances, estimation error and the effects of delay. Previous work has shown that feasibility and constraint satisfaction can be guaranteed by tightening the constraints in a suitable, monotonic sequence. This thesis extends that work in several ways, including more...

This paper considers the Bayesian counterparts of the classical tests for goodness of t and their use in judging the t of a single Bayesian model to the observed data. We focus on posterior predictive assessment, in a framework that also includes conditioning on ancillary statistics. The Bayesian formulation facilitates the construction and calculation of a meaningful reference distribution not...

Energy savings in buildings have gained a lot of attention in recent years. Most of the research is focused on the building construction or alternative energy sources in order to minimize primary energy consumption of buildings. By contrast, this thesis deals with an advanced process control technique called model predictive control (MPC) that can take advantage of the knowledge of a building m...

We present an approach to discretizing multivariate continuous data while learning the structure of a graphical model. We derive a joint scoring function from the principle of predictive accuracy, which inherently ensures the optimal trade-off between goodness of fit and model complexity including the number of discretization levels. Using the socalled finest grid implied by the data, our scori...

The focus of this chapter is on MPC of constrained dynamic systems, both linear and nonlin‐ ear, to illuminate the ability of MPC to handle constraints that makes it so attractive to in‐ dustry. We first give an overview of the origin of MPC and introduce the definitions, characteristics, mathematical formulation and properties underlying the MPC. Furthermore, MPC methods for linear or nonlinea...

In this paper we propose a cooperative distributed linear model predictive control strategy applicable to any finite number of subsystems satisfying a stabilizability condition. The control strategy has the following features: hard input constraints are satisfied; terminating the iteration of the distributed controllers prior to convergence retains closed-loop stability; in the limit of iterati...

Model predictive control (MPC) is a multivariable feedback control technique used in a wide range of practical settings, such as industrial process control, stochastic control in economics, automotive and aerospace applications. As they are able to handle hard input and output constraints, a system can be controlled near its physical limits, which frequently results in performance superior to l...