MEng Electrical and Electronic Engineering with Industrial Experience

Part A: Digital Control 
1. Motivation for digital control theory, including computer-based control. 
2. Discrete representation of continuous systems: discrete transfer functions, the z transform, and difference equation. 
3. Stability analysis. 
4. Control system design concerns in practice (e.g. sampling rate selection, computational time) in the discrete domain. 
5. Classical analysis in the discrete domain.

Part B: Model Predictive Control (MPC) 
1. 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.
2. Practical MPC implementation considerations: empirical model development, usage of design/tuning parameters, implementation/commissioning of MPC.
3. Design of MPC control for typical CSTR chemical reactor as well as distillation column. 

To select unit EEEN40241, you need to have selected unit EEEN30232 Control Systems II in your 3rd Year.

To introduce students to fundamental concepts and their applications of digital control. 

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

On successful completion of the course, a student will be able to:

ILO 1: Derive discrete-time models and relate them to practical control applications.

ILO 2: Relate classical control to digital control systems

ILO 3: Analyse digital control systems using transfer function and state space modelling techniques.

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 the unconstrained optimal control law for simple low-order single-input, single-output systems and analyse performance of the resultant closed-loop control system.

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.

Lectures, tutorials and practical labs.

Written exam, 4 questions (80%)

Digital Control Lab (10%)

MPC Control of Simulated Distillation Column (10%)

Standard exam feedback provided after the exam board. 

Digital Control - individual feedback is provided three weeks after submission.

MPC Control of Simulated Distillation Column - online submission, individual feedback is provided two weeks after submission.

Franklin, G. F., Powell, J. D., & Workman, M. L. (1998). Digital Control of Dynamic Systems (3rd ed.). Addison Wesley.

Åström, K. J., & Wittenmark, B. (Eds.). (1984). Computer Controlled Systems: Theory and Design. Prentice-Hall.

Maciejowski, J. M. (2001). Predictive Control with Constraints. Prentice Hall