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MSc Advanced Control and Systems Engineering / Course details
Year of entry: 2024
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Course unit details:
Process Control & Model Predictive Control
Unit code | EEEN60441 |
---|---|
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 |
Available as a free choice unit? | No |
Overview
BRIEF DESCRIPTION OF THE UNIT:
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.
Pre/co-requisites
Unit title | Unit code | Requirement type | Description |
---|---|---|---|
Control Fundamentals | EEEN64401 | Pre-Requisite | Compulsory |
State-Space and Multivariable Control | EEEN60109 | Pre-Requisite | Compulsory |
Aims
This course unit detail provides the framework for delivery in the current academic year 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: | |
ILO 1 | Design PID controller using Ziegler-Nichols, Cohen-Coon and Internal Model Control principle. |
ILO 2 | Design feedforward and cascade controllers as well as de-coupler-based multi-loop control systems for process control applications. |
ILO 3 | Solve real-time process optimisation problem using linear programming method. |
ILO 4 | Describe Model Predictive Control problem formulation using state-space system model format. |
ILO 5 | Convert optimisation-based control problem formulation into general mathematical programming formulation. |
ILO 6 | Derive and analyse unconstrained optimal control law for simple low-order single-input, single-output systems. |
ILO 7 | Design Model Predictive Control by selecting appropriate weights in the corresponding cost function. |
ILO 8 | 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. |
10% |
MPC Control of Simulated Distillation Column | Online submission in Blackboard, individual performance feedback is provided two weeks after students submit their answers. | 10% |
Feedback methods
.
Recommended reading
“Process Dynamics and Control”, Seborg, Edgar, Mellichamp.
“Predictive Control with Constraints”, Maciejowski.
Study hours
Scheduled activity hours | |
---|---|
Lectures | 24 |
Practical classes & workshops | 6 |
Independent study hours | |
---|---|
Independent study | 120 |
Teaching staff
Staff member | Role |
---|---|
Ognjen Marjanovic | Unit coordinator |
Alexandru Stancu | Unit coordinator |