(NOTE: All the content was found on the Internet.)

This course is intended to provide a review of modern process control engineering. The purpose of the course is to serve as an introduction to process dynamics, modeling and control. The objectives include: (a) equipping students with basic understanding of issues related to basic control algorithms, advanced control strategies, multivariable control, plant parameter estimation, and process modelling and simulation; (b) enhancing students’ skills and techniques for tackling practical process control system design problems through case studies.

Basic Control Algorithms. Model Predictive Control. Multivariable Control. Plant Parameter Estimation. Case Studies in Process Control.

On completion of this course, students should be confident to handle tasks on modelling, analysis, design and implementation of control systems for the process industry.

• J.M. Maciejowski, "Predictive Control with Constraints," Prentice Hall, 2001.
• D. E. Seborg, "Process Dynamics and Control," John Wiley & Sons, 2004.

• Camacho and Bordons, "Model Predictive Control, 2nd Edition," Springer 2004.
• L. Wang, "Model Predictive Control System Design and Implementation Using MATLAB," Springer, 2009.
• Rossiter, "Model-Based Predictive Control: A Practical Approach," CRC Press, 2003.
• B. W. Bequette, "Process Control Modeling Design and Simulation," Prentice Hall, 2003.