MECH ENG 4123 - Advanced Digital Control
North Terrace Campus - Semester 2 - 2020
General Course Information
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 4 hours per week Available for Study Abroad and Exchange N Incompatible MECH ENG 4053, MECH ENG 7034 Assumed Knowledge Continuous time Dynamics & Control e.g. MECH ENG 3028 Course Description This course concerns the design, analysis and implementation of advanced mechatronics control systems. Discrete time systems and the sampling process are examined. Digital state-space control methods are considered in detail alongside Artificial Intelligence (AI) techniques including Fuzzy Logic and Artificial Neural Networks. Emphasis is given to algorithm implementation, and implementation platforms studied include micro-controllers, digital signal processors and field-programmable gate arrays.
Course Coordinator: Associate Professor Steven Grainger
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn successful completion of this course students will be able to:
1 Apply discrete state-space design techniques; 2 Analyse discrete plant models and design digital controllers able to meet defined specifications; 3 Create digital filters to meet defined specifications; 4 Apply fuzzy logic to the design of control systems; 5 Recognise the responsibility of engineers to the community for the safety issues associated with the use of control systems; and 6 Recognise the need to undertake lifelong learning.
The above course learning outcomes are aligned with the Engineers Australia Stage 1 Competency Standard for the Professional Engineer.
The course is designed to develop the following Elements of Competency: 1.1 1.2 1.3 1.4 1.5 1.6 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 3.5 3.6
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1, 3, 4 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
2-4 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
2-4 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
5 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
5 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
Lecture Notes provided
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,
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
K. Passino, S. Yurkovich, Fuzzy Control, on-line book, [accessed: May 2006]
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.
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 24 Laboratories (optional) 6 Self study 56 Directed reading 12 Assignments 45
Learning Activities SummaryDigital 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
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 Task Weighting (%) Individual/ Group Formative/ Summative Due (week)* Hurdle criteria Learning outcomes Quizzes & exercises 25 Individual Summative Weeks 1-12 1. 2. 3. 4. Assignments 30 Individual Summative Weeks 1-12 1. 3. 4. Online Examinations 45 Individual Summative Weeks 4-12 1. 2. 3. 4. Total 100
This assessment breakdown complies with the University's Assessment for Coursework Programs Policy.
Assessment Related Requirements
Requires the design, implementation and test of digital filters & controllers. Submission of an engineering report and developed software.
Quizzes & Exercises 25%
Online Examinations 45%
All assessment must be submitted through MyUni. Late submissions are subject to a penalty of 10% per day. Re-submissions are not allowed except under extenuating circumstances. Assignments will normally be returned within 2 working weeks.
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 Access Adelaide.
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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