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...

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...

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...

An extended state space (ESS) model, familiar in subspace identification theory, is used for the development of a model based predictive control algorithm for linear model structures. In the ESS model, the state vector consists of system outputs, which eliminates the need for a state estimator. A framework for model based predictive control is presented. Both general linear state space model st...

Model predictive control (MPC) anticipates future events to take appropriate control actions. Nonlinear MPC (NMPC) describes systems with nonlinear models and/or constraints. A Continuation/GMRES Method for NMPC, suggested by T. Ohtsuka in 2004, uses the GMRES iterative algorithm to solve a forward difference approximation Ax = b of the Continuation NMPC (CNMPC) equations on every time step. Th...

We propose a distributed model predictive control approach for linear time-invariant systems coupled via dynamics. The proposed approach uses the tube MPC concept for robustness to handle the disturbances induced by mutual interactions between subsystems; however, the main novelty here is to replace the conventional linear disturbance rejection controller with a second MPC controller, as is don...

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...

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...

This work focuses on robustness of model predictive control (MPC) with respect to satisfaction of process output constraints. A method of improving such robustness is presented. The method relies on formulating output constraints as chance constraints using the uncertainty description of the process model. The resulting on-line optimization problem is convex. The proposed approach is illustrate...