MECT07025 2019 Control Systems 302
Full Title
Control Systems 302
Transcript Title
Control Systems 302
Code
MECT07025
Attendance
N/A %
Subject Area
MECT - 0719 Mechatronics
Department
MENG - Mech. and Electronic Eng.
Level
07 - Level 7
Credit
05 - 05 Credits
Duration
Semester
Fee
€
Start Term
2019 - Full Academic Year 2019-20
End Term
9999 - The End of Time
Author(s)
Kevin Collins
Programme Membership
SG_EMTRN_B07 201900 Bachelor of Engineering in Mechatronic Engineering
SG_EPLYP_J07 201900 Bachelor of Engineering in Polymer Processing
SG_EMTRN_J07 201900 Bachelor of Engineering in Mechatronic Engineering
SG_EELEC_H08 202000 Bachelor of Engineering (Honours) in Electronics and Self Driving Technologies
SG_EMTRN_J07 202000 Bachelor of Engineering in Mechatronic Engineering
SG_EROBO_H08 202000 Bachelor of Engineering (Honours) in Robotics and Automation
SG_EMSYS_B07 201900 Bachelor of Engineering in Mechatronic Systems
SG_EMECH_N07 202000 Certificate in Mechatronic Engineering
SG_EELEC_H08 202100 Bachelor of Engineering (Honours) in Electrical Engineering and Sustainability
SG_EMECS_H08 202400 Bachelor of Engineering (Honours) in Mechatronic Systems
SG_EPOLP_J07 202200 Bachelor of Engineering in Polymer Process Engineering
SG_EMECH_N07 202400 Certificate in Mechatronic Engineering
SG_EMECH_J07 202300 Bachelor of Engineering in Mechatronic Engineering
SG_EMTRN_B07 202300 Bachelor of Engineering in Mechatronic Engineering
SG_EPOLZ_J07 202200 Bachelor of Engineering in Polymer Process Engineering
SG_EPOLY_J07 202400 Bachelor of Engineering in Polymer Processing
SG_EPOLA_J07 202400 Bachelor of Engineering in Polymer Process Engineering
SG_EPOLB_J07 202500 Bachelor of Engineering in Polymer Process Engineering
SG_EROBO_H08 202400 Bachelor of Engineering (Honours) in Robotics and Automation
SG_EMSYS_B07 202400 Bachelor of Engineering in Mechatronic Systems
SG_EPLYP_J07 202400 Bachelor of Engineering in Polymer Processing
SG_EROBO_H08 202500 Bachelor of Engineering (Honours) in Robotics and Automation
Description
Control Systems is all about plant and processes (systems) how they behave when subjected to certain inputs (system response) and how to get them to do what we want (system control). Control Systems 302 introduces the student to analog and digital strategies for controlling these systems
Learning Outcomes
On completion of this module the learner will/should be able to;
1.
Carry out practicals using analog control techniques on mechanical and fluid equipment.
2.
Derive the difference equations for numerical integrators and differentiators.
3.
Test for discrete system stability by location of the z-plane poles.
4.
Design digital controllers using cancellation pole placement, one-step-ahead and Kalman and Diophantine pole placement strategies.
5.
Implement simple machine learning strategies in linear regression, logistic regression and neural networks using Matlab/Octave software.
6.
Use software (e.g. LabView, Simulink) to tune PID controllers.
Teaching and Learning Strategies
.
Module Assessment Strategies
Final exam 60%
Practical reports 20%
Continuous assessment 20%
Repeat Assessments
.
Module Dependencies
Prerequisites
MECT07005 201300 Control Systems 301
Indicative Syllabus
Analog Systems
PID control techniques
Discrete Systems
Signal sampling and the z-transform,
Difference equations and the pulse transfer function.
Numerical integration and integration.
A/D conversion and the zero-order hold (ZOH) device.
Z-plane stability.
Discrete Control
Discretised PID control.
Controller design by cancellation pole placement (CPP).
One step ahead (OSAC) and dead-beat control strategies.
Dahlin controller design.
Diophantine pole placement controller design.
Indicative Practicals/Projects
Use of a proprietary laboratory apparatus (e.g. L.J. Technical Systems, Data Acquisition of Control Systems) and software packages (e.g. , Matlab, Labview) to investigate the following:
Controller tuning by pole-placement
Properties of the zero-order hold device
Examples of controller excursions and "ringing" poles
Kalman and Diophantine controller design
Machine learning
Linear regression, logistic regression, neural networks.
Coursework & Assessment Breakdown
Coursework & Continuous Assessment
40 %
End of Semester / Year Formal Exam
60 %
Coursework Assessment
| Title | Type | Form | Percent | Week | Learning Outcomes Assessed | |
|---|---|---|---|---|---|---|
| 1 | Other Exam Supervised and unsupervised quizzes | Coursework Assessment | UNKNOWN | 20 % | OnGoing | 2,3,4,5,6 |
| 2 | Written Report of practicals | Coursework Assessment | UNKNOWN | 20 % | OnGoing | 1,2,3,4,5,6 |
End of Semester / Year Assessment
| Title | Type | Form | Percent | Week | Learning Outcomes Assessed | |
|---|---|---|---|---|---|---|
| 1 | Final Exam | Final Exam | UNKNOWN | 60 % | End of Term | 3,4,5,6 |
Full Time Mode Workload
| Type | Location | Description | Hours | Frequency | Avg Workload |
|---|---|---|---|---|---|
| Practical / Laboratory | Engineering Laboratory | Pratical | 2 | Weekly | 2.00 |
| Tutorial | Flat Classroom | Theory | 2 | Weekly | 2.00 |
Total Full Time Average Weekly Learner Contact Time 4.00 Hours
Module Resources
Non ISBN Literary Resources
|
Authors |
Title |
Publishers |
Year |
|
W Bolton |
Control Engineering |
Longman |
1998 |
|
Burns |
Advanced Control Engineering |
Butterworth Heineman |
2002 |
|
Leigh |
Applied Digital Control |
Prentice Hall |
2007 |
|
Nise |
Control Systems Engineering |
Wiley |
2013 |
|
|
|
|
|
Journal Resources
.
URL Resources
.
Other Resources
None
Additional Information
None