The course of Process Modeling is directed to provide a modern review of process parameter identification in process control engineering. Hence, it is important to learn parameters identification methods and process control systems. The objectives include equipping learners with (a) understanding of issues related to parameters identification including fundamental concepts, step input, relay feedback, time domain and frequency domain identification method; and (b) case studies in process parameter identification 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 parameter identification in the practical field of industrial process control. Specifically, they will understand the modeling of processes and how to obtain the parameter in the design of control systems. They will understand various identification methodologies applied to the process control systems. Extensive parameter identification simulation projects and case studies will provide the learners with an insight into the actual application of modelling techniques in the industry processes;
Basic concepts of process modelling, graphic method, two point method, log method, area method, least squares method, multivariable process identification.
Closed-loop step input identification method
Least squares method, Fourier transformation, closed-loop identification in time domain, closed-loop identification in frequency domain, recursive identification method.
Fundamental of relay feedback, ultimate frequency, simple identification method, frequency domain identification method, enhanced relay feedback
Concept and theoretical basis of model predictive control
Fundamental of model predictive control, the concept of model predictive control, the design parameters of model predictive control, the challenges of model predictive control, the applications of model predictive control.
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)
Enhanced Training Support for SMEs (ETSS)
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|
|CET728 Model predictive control||1|
|CET729 Multivariable process 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.