Course Objectives

• Acquire an understanding for optimization of dynamic systems, with special emphasis on predictive control, and an overview of how advanced control algorithms can be implemented, how the algorithm can be coupled to a practical system/a process, and the importance of the control algorithm in the process control system.

Course Description

Mathematical programming/optimization methods. Optimization of dynamical systems (overview): model fitting, state estimation, data reconciliation. Discrete and continuous time systems, and parameterization of high order problems. Predictive control in LQ systems: DMC, GPC, state space models. Offset-free predictive control, and robust predictive control. Predictive control in nonlinear systems; efficient solution methods. Predictive control and controller efficiency. Overview of strategies for implementing control algorithms. Overview of commercially available predictive controllers.

Learning Methods

Lectures, exercises, a compulsory group project, and the use of relevant software for PCs.

Assessment Methods

Five obligatory exercises should be delivered and this counts 30 % of the final degree.

It is necessary to pass the final examination in order to achieve a passing grade.

Minor adjustments may occur during the academic year, subject to the decision of the Dean