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Introduction
The course of Process Modeling is directed to provide a modern review of the model predictive control. Hence, it is important to learn the concThe course of Multivariable Process Control is directed to provide a modern review of multivariable process control engineering. Hence, it is important to learn multivariable control, decentralized control and centralized control. The objectives include equipping learners with (a) understanding of issues related to Multivariable Control strategies including fundamental concepts, loop pairing, decentralized control and decoupling control; and (b) case studies in multivariable process control system to enhance skills and techniques for tackling practical multivariable process control system design problems.
This course is part of:
- Graduate Certificate in Computer Control and Automation
- FlexiMasters
in Computer Control and Automation
On completion of the course, the learners should be able to understand t he role and relevance of multivariable control theory to the practical field of industrial process control. Specifically, they will understand the interaction analysis of multivariable processes and its uses in the design of control systems. They will understand various controller-tuning methodologies applied to the multivariable control systems. They will become aware of different practical control strategies as well as advanced control techniques that are specifically suited for the multivariable processes. Extensive multivariable control system simulation projects and case studies will provide the students with an insight into the actual application of multivariable control techniques in the industry processes.
Review of Process control strategies. PID control system design. Direct Synthesis Control, Internal Model Control, Gain and Phase Margin Control.
Fundamental concepts, interaction analysis, static loop pairing, dynamic loop pairing, semi-dynamic loop pairing, stability.
RGA based detuning, process decomposition method, BLT method, Equivalent transfer based design.
Simplified decoupling, generalized decoupling, inverted decoupling, Normalized decoupling, sparse control, block decoupling.
Standard Course Fee: S$1,620
SSG Funding Support | Course fee payable after SSG funding, if eligible under various schemes | ||
Fee BEFORE funding & GST | Fee AFTER funding & 8% GST | ||
Singapore Citizens (SCs) and Permanent Residents (PRs) (Up to 70% funding) | S$1,500.00 | S$486.00 | |
Enhanced Training Support for SMEs (ETSS) | S$186.00 | ||
SCs aged ≥ 40 years old | |||
• NTU/NIE alumni may utilise their $1,600 Alumni Course Credits. Click here for more information.
| COURSE TITLE | ACADEMIC UNIT |
| CET715 Discrete Time System Modelling and Analysis | 1 |
| CET716 State-Space Design Methods | 1 |
| CET717 Optimal Control, Design and Implementation of Digital Controllers | 1 |
| CET718 Linear and Nonlinear Programming | 1 |
| CET719 Random Processes and Queuing Models | 1 |
| CET720 Decision Analysis | 1 |
| CET721 Robot Kinematics | 1 |
| CET722 Robot Control | 1 |
| CET723 Robotic Sensing and Sensors | 1 |
| CET724 Image Processing for Vision | 1 |
| CET725 Pattern Recognition and Stereo Vision | 1 |
| CET726 Three-dimensional computer vision | 1 |
| CET727 Process modelling | 1 |
| CET728 Model Predictive Control | 1 |
Listed courses are:
- Credit-bearing and stackable to Graduate Certificate in Computer Control and Automation (total 9AUs) and FlexiMasters in Computer Control and Automation (total 15AUs).
- SSG funded and SkillsFuture Credit approved.
