CET728 Model Predictive Control

Course Provider

School of Electrical and Electronic Engineering (EEE)



Academic Unit



The course of Process Modeling is directed to provide a modern review of the model predictive control. Hence, it is important to learn the concepts of model predictive control and their applications in power grids. The objectives include equipping learners with (a) understanding of the basic theory of model predictive control (main components, modelling and different model based predictive control); and (b) case studies in model predictive control, especially in the smart grid application. . 

This course is part of:
- Graduate Certificate in Computer Control and Automation
- FlexiMasters  in Computer Control and Automation

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On completion of the course, the learners should be able to:

1. Understand the concept and structure of the MPC;

2. Can use different model based MPC, including Model predictive control with state space model / Carima model/ step response models;

3. Can design the parameters, cost function and constrains of the MPC and; 

4. Know how to use MPC in the smart grid, including design MPC-based controller for DC/DC converters and DC/AC inverters.

Basic theory of the model predictive control: Part I

Main component of model predictive control, Modelling of model predictive control, model predictive control with state space model, model predictive  control with Carima model.

Basic theory of the model predictive control: Part II

Model predictive control with impulse/step response models, model predictive control to ensure unbiased prediction, parameters selection rules  for the model predictive control.

Application of model predictive control in smart grid: Part I

Application of model predictive control in smart grid, the concept of finite control set model predictive control (FCS-MPC), the application of FCS-MPC  in power inverters in the smart grids.

Application of model predictive control in smart grid: Part II

The limitation of traditional PID control in smart grid application, model predictive control for the DC/DC converters in smart grids, overview and  design issues of the model predictive control in smart grids.

Automation Engineers for upgrading and Engineers who wish to switch to the automation field.

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
SkillsFuture Mid-career Enhanced Subsidy (MCES)
(Up to 90% funding)

• NTU/NIE alumni may utilise their $1,600 Alumni Course Credits. Click here for more information.

Read more about funding
CET715 Discrete Time System Modelling and Analysis1
CET716 State-Space Design Methods1
CET717 Optimal Control, Design and Implementation of Digital Controllers1
CET718 Linear and Nonlinear Programming1
CET719 Random Processes and Queuing Models1
CET720 Decision Analysis1
CET721 Robot Kinematics1
CET722 Robot Control1
CET723 Robotic Sensing and Sensors1
CET724 Image Processing for Vision1
CET725 Pattern Recognition and Stereo Vision1
CET726 Three-dimensional computer vision1
CET727 Process modelling1
CET729 Multivariable process control1

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.