Process Control II



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

Course Description

This course presents methods for dynamic analysis and controller design for multivariable process control problems, and discrete time control. Control techniques, including anti-reset windup, internal model principle, feedforward and cascade control, are discussed and analyzed.  An introduction to the theory of Model predictive control is presented. Multivariable controller design and the problem of control loop interaction are examined. State space models for processes are introduced. Mathematical tools for analyzing the dynamics of sampled data systems are developed, and the design of discrete time controllers is introduced. Techniques discussed in the course are applied to the control of various chemical process units. (0/0/0/30/12)

PREREQUISITES: CHEE 319 or permission of the department.

Objectives and Outcomes

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 or mechanistic models and for which a satisfactory description of the model uncertainties have been characterised. Both state-space and input-output modeling formulations will be considered. Since most models cannot represent the behaviour 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. We also revisit frequency response analysis techniques such as Bode diagrams, the Nyquist stability criterion and introduce a robust stability criterion for a class of 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.

Specific course learning outcomes include:

CLO1 Recognize the importance of modeling errors and uncertainties in controller design. KB-Proc(d)
CLO2 Apply modern control theory to design a controller for uncertain SISO and MIMO linear dynamical systems KB-Proc(d)
CLO3 Understand the trade-off in performance that arise in the design of a controller. KB-Proc(d)

This course assesses the following program indicators:

Knowledge base for engineering (KB)

  • KB-Proc(d) Derives transfer function models from dynamic process models and process data to apply control theory.

Design (DE)

  • DE-Define Define problem, objectives and constraints.
  • DE-Solutions Create a product, process or system to solve a problem, that meets specified needs, and subject to appropriate iterations.
  • DE-Assess Evaluate performance of a design, using criteria that incorporates specifications, limitations, assumptions, constraints, and other relevant factors.

Relevance to the Program

Course Structure and Activities

3 lecture hours + 1 tutorial hour per week. Please refer to SOLUS for times and locations.

Lecture slides will be posted in advance. Some lectures will include examples and problem solutions not contained in the posted slides. Students are expected to read associated sections and study worked examples in the textbook. Students are expected to bring a copy of the tutorial problem (posted in advance) to class.


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

Other Material
Matlab / Simulink are available in the computer cluster, Dupuis Hall, and in the teaching studio (Room 213, Beamish-Munro Hall).
All course lecture slides, assignments and tutorials will be posted on the course website, or Learning Management System.