ME603

Unit Description and General Aims

The subject quickly moves from a review of process control fundamentals to multivariable control where the student will gain a deep understanding of the key principles ranging from nature of multivariable systems, process models to interaction analysis, loop pairing and relative gain arrays. The student is then exposed to a detailed review of digital process control and its application. A detailed examination is then performed of model predictive control ranging from dynamic matrix control, model algorithm control to design concepts. An in-depth application of statistical process control with advanced process control is then undertaken. The course is concluded by a study of advanced topics in process control with an emphasis on the application of the technologies.

Learning Outcomes

On successful completion of this Unit, students are expected to be able to:

1. Demonstrate a deep understanding of process control fundamentals
    Bloom’s Level 6
2. Apply key principles of multivariable control in a range of contexts
    Bloom’s Level 6
3. Demonstrate a thorough understanding and application of digital process control as
    compared to the older analogue forms
    Bloom’s Level 6
4. Assess applications for and be able to apply model predictive control within a variety
    of contexts
    Bloom’s Level 5
5. Justify and be able to apply statistical process control at an advanced level
    Bloom’s Level 5
6. Demonstrate an in-depth understanding of advanced process control across a wide
    variety of contexts
    Bloom’s Level 6

Student assessment

Prescribed and Recommended Readings

Required textbook

• Process Control: Theory and Applications by Jean-Pierre Corriou ISBN 185-233-7761

Recommended:

• Terrence Blevins,T., Wojsznis, W.K. & Nixon, M. (2012) Advanced Control Foundation: Tools,
  Techniques, and Applications. Industrial Society for Automation. Raleigh, USA. ISBN 978-
  1937560553
• The Control Handbook (Electrical Engineering Handbook), 1996 by William S. Levine (Editor)
  ISBN-13: 978-0849385704

Reference Materials
Number of peer-reviewed journals and websites (advised during lectures). Some examples are listed
below.

1. Perry’s Chemical Engineers Handbook, 8th edition, McGraw Hill (earlier editions are
    acceptable)
2. IEEE Transactions on Automatic Control
3. IEEE Transactions on Automation Science and Engineering
4. IEEE Transactions on Instrumentation and Measurement
5. IEEE Instrumentation and Measurement Magazine
6. Automation World Magazine
7. Manufacturing Automation Magazine
8. Managing Automation
9. IDC notes and Reference texts as advised.
10. Other material advised during the lectures

Unit Content

One topic is delivered per contact week, with the exception of part-time 24-week units, where one topic is delivered every two weeks.

Topic 1

Review of process control fundamentals - 1

1. Control elements
2. Process dynamics
3. Transfer functions
4. Factors making control difficult

Topic 2

Review of process control fundamentals - 2

1. Feedback control
2. Feedforward control
3. Cascade control
4. PID Controller tuning

Topic 3

Introduction to multivariable control - 1

1. The nature of multivariable systems
2. Process models
3. Linear models and linearization
4. Experimental models

Topic 4

Introduction to multivariable control - 2

1. Interconnection of systems
2. Linear systems analysis
3. Solutions to the control problem
4. Singular Value Decomposition (SVD) for multivariable control

Topic 5

Introduction to multivariable control - 3

1. Multivariable control structures
2. Relative Gain Array (RGA) strategy
3. Decoupled control

Topic 6

Digital (computerized) process control

1. Discrete time systems
2. Concepts of z-Transforms
3. Sampling effects
4. Discretisation of continuous controllers
5. Implementation of discrete PID controllers

Topic 7

Optimal control

1. Unconstrained optimisation
2. Constrained optimisation
3. Multivariable quadratic optimisation
4. Optimal control

Topic 8

Linear Quadratic Regulator

1. State feedback
2. Linear Quadratic Regulator (LQR) solution
3. Reference tracking using LQR

Topic 9

Model predictive control - 1

1. Model Predictive Control (MPC)
2. MPC components
3. Handling constraints
4. Control and prediction horizons

Topic 10

Model predictive control - 2

1. MPC simulation
2. Model identification
3. Observers

Topic 11

Statistical process control

1. Introduction to Statistical Process Control (SPC)
2. Variation and its management
3. Control charts
4. Multivariable statistical process control

Topic 12

Project and/or Unit Review

In the final week students will have an opportunity to review the contents covered so far. Opportunity will be provided for a review of student work, to clarify any outstanding issues, and to work on finalizing the major assessment report.

Engineers Australia

The Australian Engineering Stage 1 Competency Standards for the Professional Engineer, approved as of 2013. This table is referenced in the mapping of graduate attributes to learning outcomes and via the learning outcomes to student assessment.

Software/Hardware Used

Software

• Software: Matlab (Toolboxes: Control System Toolbox, System Identification Toolbox, Statistics and Machine Learning Toolbox, Model Predictive Control Toolbox)
• Version: R2019a
• Instructions: Install the Student version on your computer OR use the software on Remote lab
• Additional resources or files: N/A

Hardware

• N/A

Unit Changes Based on Student Feedback