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Chemical Engineering

CHEE434

CHEE434 : Process Control II

Personnel

Instructor

Martin GuayDupuis 406guaym@queensu.ca613-533-2788

Course Description

The basic objective of this course is to provide a comprehensive introduction to the concept of controller design for dynamical control systems. We will consider primarily a model-based approach where the dynamics of the process to be controlled have been modeled adequately using either black box ormechanistic models. Both state-space and input-output modeling formulations will be considered. Since most models cannot represent the behavior of a given process exactly, the effect of modeling errors on controller design will form a consistent theme throughout this course. Throughout the course, the students will consider some of the most prominent controller design techniques currently available. We will first emphasize the development of control system analysis tools for continuous-time and discrete-time linear systems. These include frequency response analysis techniques such as the Nyquist stability criterion and the robust stability criterion for uncertain linear systems.

The primary emphasis will be on controller design techniques, in particular, model-based controller design. We will first consider the design for single-input/single-output (SISO) continuous time and discrete time linear systems. The course will attempt to assemble a set of tools for the design of controller in the presence of delay, model uncertainties and process disturbances.

One of major challenges in this course (and control engineering practice) is the design of controllers for Multi-input/Multi-output systems (MIMO). We will first consider generalizations of the techniques developed for SISO systems. More general techniques based on the state-space will also be considered.

At the final stage of the course, we will study optimization-based control techniques and, in particular, model predictive control (MPC). MPC has been widely recognized in the chemical and petrochemical industry and forms the basis of most industrial multivariable controllers.

Objectives and Outcomes

By the end of this course the student should be able to:

  • recognize the importance of modeling errors and uncertainties in controller design
  • apply modern control theory to design a controller for uncertain SISO and MIMO linear dynamical systems
  • understand the trade-off in performance that arise in the design of a controller

Relevance to the Program

Course Structure and Activities

Schedule

  • Lectures: (DUP 311) Tuesday 9:30-10:20, Thursday 8:30-9:20, Friday 10:30-11:20
  • Tutorials: (ILC 213) Monday 14:30-15:20
  • Office Hours (MG): Monday 15:30-17:00, Wednesday 15:30-17:00 (Tentative)

Resources

G.C. Goodwin, S.F. Graebe and M.E. Salgado, Control System Design¸ Prentice Hall, Upper Saddle River, NJ (2001);

View the textbook website at http://csd.newcastle.edu.au/control/