MECH ENG 4123 - Advanced Digital Control
North Terrace Campus - Semester 2 - 2016
The course information on this page is being finalised for 2016. Please check again before classes commence.
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General Course Information
Course Details
Course Code MECH ENG 4123 Course Advanced Digital Control Coordinating Unit School of Mechanical Engineering Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Y Incompatible MECH ENG 4053, MECH ENG 7034 Assumed Knowledge Continuous time Dynamics & Control e.g. MECH ENG 3028 Assessment Assignments 30%, Final exam 70% Course Staff
Course Coordinator: Associate Professor Steven Grainger
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
1 Have a deep understanding of discrete state-space design and the implementation of digital controllers 2 Be able to analyse discrete plant models and design digital controllers able to meet defined specifications 3 Have a good understanding of digital filters and be able to design them for attenuating noise; 4 Have a good understanding of fuzzy logic and be able to apply fuzzy logic within mechatronics control systems 5 Understand the responsibility of engineers to the community for the safety issues associated with the use of control systems; 6 Understand the need to undertake lifelong learning. University Graduate Attributes
No information currently available.
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Learning Resources
Required Resources
Lecture Notes provided
Recommended Resources
Emmanuel C. Ifeachor, Barrie W. Jervis, Digital Signal Processing – A Practical Approach, 2nd Edition, Prentice Hall, 2002, ISBN 0201-59619-9
B. P. Lathi, Linear Systems and Signals, Oxford University Press (2nd ed.), July 2004,
ISBN-0-195-15833-4
Franklin G. F., Powell J. D., Workman M., Digital Control of Dynamic Systems, 3rd ed., Prentice Hall, 1997, ISBN 0201820544
Stuart J. Russell, Peter Norvig, Artificial Intelligence: A Modern Approach, 2nd Edition,
Prentice Hall, 2002, ISBN 0137903952
Online Learning
K. Passino, S. Yurkovich, Fuzzy Control, , [accessed: May 2006]
MyUni
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Learning & Teaching Activities
Learning & Teaching Modes
The course takes a flexible approach to teaching and learning with material delivered, concepts explored and skills developed using a range of techniques. A flipped model is utilised with interactive sessions used for presentation of material, exploration of concepts and discussion of directed reading. A series of diagnostic quizzes is used to establish existing knowledge and the assimilation of taught concepts.
Laboratories are centred upon project based learning with case studies used to provide hands-on experience.
Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Indicative workload is 13 hours per week
Activity Hours Interactive lecture sessions 12 Online activities 12 Laboratories 24 Self study 56 Directed reading 12 Assignments 40 Learning Activities Summary
Digital signal processing (DSP) [4 weeks]- z-Transform and its application
- Fast Fourier Transform and signal spectra
- Aliasing, over-sampling and decimation
- Signal-to-Noise ratio (SNR)
- Fixed-point and floating point arithmetic
- Digital filters: FIR/IIR filter design
- Micro-controller & DSP based implementation of FIR and IIR filters
- FPGA based FIR filter implementation (distributed arithmetic)
- Adaptive filters
- Design by emulation of continuous-time systems
- Discretization of continuous-time systems (ZOH, Tustin, forward rectangular)
- Discrete state-space models
- Canonical forms (controller, observer, modal, Jordan)
- Direct digital control design
- Prediction estimators (full order, reduced order)
- Current estimators (full order, reduced order)
- Tracking systems
- System Identification
- Overview of Artificial Intelligence in robotics and mechatronics applications
- Introduction to fuzzy logic, fuzzy statements, fuzzy sets, and fuzzy control
- Fuzzification, fuzzy-inference and defuzzification
- Fuzzy logic simulation packages
- Fuzzy logic control applications
- Fuzzy identification and estimation
- Artificial Neural Networks
- Neuro-Fuzzy technology
Specific Course Requirements
N/A
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Assessment
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
Assessment Task Task Type Due Weighting Learning Outcome Assignment 1 Summative 5 15% 3 Assignment 2 Summative 11 15% 1,2,4 Online quizzes Summative 1-12 10% 1-6 Lab sessions Summative 1-12 10% 1-6 Examination Summative 50% 1-4 Assessment Related Requirements
N/A
Assessment Detail
Assignment 1 15%
Requires the design, implementation and test of digital filters. Submission of an engineering report and developed software.
Assignment 2 15%
Requires the design and implementation of a control system for mechatronics devices. Student demonstration and submission of developed software.
Online Quizzes 10%
An online quiz accommpanies each topic.
Lab Sessions 10%
Students are required to undertake the weekly lab sessions.
Examination 50%2hr open book examination.
Submission
Fully commented source code and associated assignment documentation must be submitted through MyUni. Late submissions are subject to a penalty of 10% per working day. Re-submissions are not allowed except under extenuating circumstances. Assignments will normally be returned within 2 working weeks.
Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through .
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Student Feedback
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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