Studying at York
Advanced control - ELE00156M
- Department: Electronic Engineering
- Credit value: 20 credits
- Credit level: M
- Academic year of delivery: 2024-25
- See module specification for other years: 2023-24
Module summary
This module follows on from Control and Instrumentation, and starts by introducing the state-space system representation. This allows a number of increasingly advanced control algorithms to be investigated, including pole placement, eigenstructure assignment, observers, and optimal control techniques such as LQR. The culmination of the module is a detailed look at Model Predictive Control (MPC), a computationally-intensive but highly effective control strategy which is gaining considerable traction in industry
Professional requirements
Related modules
Pre-requisite modules
Co-requisite modules
- None
Prohibited combinations
- None
Additional information
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Semester 1 2024-25 |
Module aims
Subject content aims:
-
To provide insights into the impact of introducing samplers into feedback control systems, including the use of the Z-transform, the purpose of data-holds and the calculation of inverse Z-transforms
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To develop an understanding of the importance of the concept of the state of a control system and to provide an introduction to the techniques of state-variable control, in continuous and discrete time, including the state representation, the state transition matrix, state-variable feedback and output feedback
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To introduce dynamic compensation and state observers as two alternative approaches to addressing the problem of insufficient freedom in output feedback problems
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To introduce state-feedback eigenstructure assignment as an extension to pole placement
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To introduce cost-function based control design techniques, including optimal control (LQR) and model-predictive control (MPC)
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In particular, to provide familiarity with MPC, due to its widespread adoption in industrial process control applications and its continued profile as a topic of academic research, thus preparing students for both research and industrial employment
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To explore the nature of MPC constraints, including terminal point and terminal region constraints
Graduate skills aims:
-
To develop critical skills in the selection, adaptation and application of appropriate numeric and algebraic techniques
Module learning outcomes
Subject content learning outcomes
After successful completion of this module, students will be able to:
- Apply classical control techniques to discrete-time systems
- Design control systems for multi-input, multi-output systems using a range of algorithms
- Evaluate and compare different optimal control algorithms, and provide a comparison with classical approaches
- Derive and prove equations relating to the implementation of continuous- and discrete-time LQR and MPC control
- Design LQR and MPC controllers using Matlab and, for simple systems, by hand
- Explain and evaluate advanced technical concepts concisely and accurately
- Select, adapt and apply a range of mathematical techniques to solve advanced problems
Graduate skills learning outcomes
After successful completion of this module, students will be able to:
- Express advanced technical concepts concisely and accurately and comment on their applications, limitations and implications
- Select, adapt and apply a range of mathematical techniques to solve advanced problems and explain the implications of the answer
- Demonstrate skills in problem solving, critical analysis and applied mathematics
Module content
Sampling and the Z transform
Control in the Z domain - root locus and PAN design
The bilinear transform
State-space representation and conversion to/from transfer function form
Eigenvalues, eigenvectors, the state transition matrix
Pole placement via state feedback and output feedback
Dynamic compensation and state observers
State-feedback eigenstructure assignment
Optimal control and LQR (continuous and discrete time)
Lyapunov stability
Model predictive control - prediction, constraints, stability
Quadratic programming
Indicative assessment
| Task | % of module mark |
|---|---|
| Closed/in-person Exam (Centrally scheduled) | 50 |
| Essay/coursework | 50 |
Special assessment rules
None
Additional assessment information
The coursework component is a MATLAB exercise, with the submission being MATLAB code, which is why the length is unspecified.
Indicative reassessment
| Task | % of module mark |
|---|---|
| Closed/in-person Exam (Centrally scheduled) | 50 |
| Essay/coursework | 50 |
Module feedback
'Feedback’ at a university level can be understood as any part of the learning process which is designed to guide your progress through your degree programme. We aim to help you reflect on your own learning and help you feel more clear about your progress through clarifying what is expected of you in both formative and summative assessments. A comprehensive guide to feedback and to forms of feedback is available in the Guide to Assessment Standards, Marking and Feedback.
The School of PET aims to provide some form of feedback on all formative and summative assessments that are carried out during the degree programme. In general, feedback on any written work/assignments undertaken will be sufficient so as to indicate the nature of the changes needed in order to improve the work. The School will endeavour to return all exam feedback within the timescale set out in the University's Policy on Assessment Feedback Turnaround Time. The School would normally expect to adhere to the times given, however, it is possible that exceptional circumstances may delay feedback. The School will endeavour to keep such delays to a minimum. Please note that any marks released are subject to ratification by the Board of Examiners and Senate. Meetings at the start/end of each term provide you with an opportunity to discuss and reflect with your supervisor on your overall performance to date.
Indicative reading
Notes and readings will be provided in workshops.