Centre for Intelligent Machines

McGill University

Advanced Industrial Control

This course presents an overview of advanced controller design strategies for multivariable industrial processes, starting from the familiar PID control structure to the more advanced H-infinity design technique.

 

The theory behind new multivariable control algorithms will be presented briefly, as the emphasis is rather being put on design techniques and tools. Simple yet realistic process examples, including a heated water tank and a room heating system, are used to illustrate the controller design techniques. Finally, current technologies for implementing the controllers are discussed.

Dr. Benoit Boulet, Eng., is an expert on industrial control. Dr. Boulet is Director of the Centre for Intelligent Machines and Associate Chair (Operations) of the Department of Electrical and Computer Engineering at McGill University. Prior to becoming a professor at McGill, Dr. Boulet worked as a consulting control engineer in the mining and metals industry.

Feedback control makes industrial machines and processes much more efficient.

 

This course presents an overview of advanced controller design strategies for multivariable industrial processes, starting from the familiar PID control structure to the more advanced H-infinity design technique.

1-day course for industry

Date: August 21, 2009

Time: 9:00-16:00

$650(CAD) per person, $200(CAD) for full-time students. This fee includes a lunch meal and a course pack composed of course notes, the book "Fundamentals of Signals & Systems" by Benoit Boulet, and a CD-ROM disc containing Matlab control design examples.

Payment by credit card only (MasterCard or Visa). Please call Ms. Marlene Gray to register at 514-398-4132. We reserve the right to cancel the course if an insufficient number of people are registered six weeks before the set date, so please make your travel arrangements accordingly. The course registration fee will be fully reimbursed in case of cancellation.

About the Course

Fee

Dr. Benoit Boulet, Eng., Course Instructor

Centre for Intelligent Machines
McGill University
McConnell Engineering Building
4th floor
3480 University Street
Montreal, Quebec, Canada
H3A 2A7

Click here for directions

Contact B. Boulet: 514-398-1478

e-mail: boulet@cim.mcgill.ca

This is a one-day intensive course taught in English and presenting the state-of-the-art in robust industrial control design techniques. The course is at the level of a senior undergraduate electrical engineering or mechanical engineering student.

 

AM

1. Background on industrial feedback control systems
-Industrial processes and need for feedback
-Control system technology (actuators, sensors, controllers)
-Classical control strategies and some applications
-Process Modeling (First principles, State-space models, Transfer functions, Linearization)
-Simulation

2. PID control
-Tuning rules
-Loop shaping
-Robustness
-Examples

3. State feedback
-Pole placement
-Observers
-Observer-based control
-Example

4. Nominal stability and performance of feedback control systems
-Nominal internal stability
-Sensitivity and complementary sensitivity functions
-Nominal performance for tracking and regulation
-Naive approach to LTI controller design (desired sensitivity approach)

5. H_oo-optimal control
-Problem setup and objective
-L₂ norm for finite-energy signals, H_oo  norm of stable systems
-Weighting functions for closed-loop sensitivity shaping
-H_oo-optimal LTI controller design using Riccati equations
-Examples

PM

6. Uncertainty modeling for robust control
-Additive and multiplicative uncertainty
-Parametric and structured uncertainty
-Examples

7. Robust closed-loop stability and performance
-Definition
-The small-gain theorem
-General robust stability criterion using a linear fractional uncertainty model
-Definition of the structured singular value m
-General robust performance criterion using a linear fractional uncertainty model

8. Robust H_oo control
-Objective
-Sufficient condition for nominal performance and robust stability
-Weighting functions for uncertainty bounds and performance specification
-Mixed-sensitivity robust H_oo controller design
-Robust performance for plant models with structured uncertainty
-Practical considerations
-Examples

9. Technologies for Controller Implementation
-PLC
-DCS
-PC-based control
-Matlab
-LabView
-Dedicated hardware

 

Course Outline