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MSc Advanced Control and Systems Engineering / Course details

Year of entry: 2022

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Course unit details:
Process Control & Model Predictive Control

Unit code EEEN60112
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Offered by Department of Electrical & Electronic Engineering
Available as a free choice unit? No



Part A: Process Control

  • PID controller structure: ideal/parallel/series, position/velocity implementation of digital PID.
  • PID controller tuning: Ziegler-Nichols and Cohen-Coon, Internal Model Control with lambda-tuning.
  • Enhanced control: cascade control, feedforward control, multi-loop control utilising de-couplers, Smith Predictor.
  • Multi-loop interaction analysis using Relative Gain Array.
  • Real-time process optimisation using Linear Programming technique.


Part B: Model Predictive Control (MPC)

  • MPC control formulation: simple unconstrained optimal control formulation, general characteristics of MPC formulation, translation of MPC problem into quadratic programming optimisation problem, constant output disturbance observer model, infeasibility and softening of the constraints.
  • Practical MPC implementation considerations: empirical model development, usage of design/tuning parameters, implementation/commissioning of MPC.
  • Design of MPC control for typical CSTR chemical reactor as well as distillation column.



Unit title Unit code Requirement type Description
Control Fundamentals EEEN60108 Pre-Requisite Compulsory
State-Space and Multivariable Control EEEN60109 Pre-Requisite Compulsory


This course unit detail provides the framework for delivery in 2020/21 and may be subject to change due to any additional Covid-19 impact.  Please see Blackboard / course unit related emails for any further updates.

The course unit aims to:

1. Introduce students to the fundamental concepts of applied industrial process control, including cascade and feedforward control structures as well as the use of de-couplers.

2. Introduce students to the formulation and the main implementation details regarding Model Predictive Control (MPC) as well as the real-time process optimisation.


Learning outcomes

All intended learning outcomes below are Developed and Assessed. On the successful completion of the course, students will be able to:


Design PID controller using Ziegler-Nichols, Cohen-Coon and Internal Model Control principle.


Design feedforward and cascade controllers as well as de-coupler-based multi-loop control systems for process control applications.


Solve real-time process optimisation problem using linear programming method.


Describe Model Predictive Control problem formulation using state-space system model format.


Convert optimisation-based control problem formulation into general mathematical programming formulation.


Derive and analyse unconstrained optimal control law for simple low-order single-input, single-output systems.


Design Model Predictive Control by selecting appropriate weights in the corresponding cost function.


Summarize the key steps of implementing Model Predictive Control, including development of empirical prediction model and the procedure of controller commissioning.


Teaching and learning methods

This unit is taught during 6-week period during which several 3-hour lectures take place every week and two 3-hour lab sessions are scheduled to take place during the 6-week period.


Assessment methods

Method Weight
Other 20%
Written exam 80%

Coursework Assessment task

How and when feedback is provided

Weighting within unit (if relevant)



Motor Speed and Position Control Lab

Online submission in Blackboard, individual performance feedback is provided two weeks after students submit their answers.



MPC Control of Simulated Distillation Column

Online submission in Blackboard, individual performance feedback is provided two weeks after students submit their answers.



Recommended reading

“Process Dynamics and Control”, Seborg, Edgar, Mellichamp.

“Predictive Control with Constraints”, Maciejowski.


Study hours

Scheduled activity hours
Lectures 42
Practical classes & workshops 6
Independent study hours
Independent study 102

Teaching staff

Staff member Role
Ognjen Marjanovic Unit coordinator
Alexandru Stancu Unit coordinator

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