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.

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.

  • 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
    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 Staff

    Course Coordinator: Associate Professor Steven Grainger

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • 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.

  • 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, on-line book, [accessed: May 2006]

    MyUni

  • 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
    Discrete state-space control [4 weeks]
    • 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
    Artificial Intelligence (AI) based technologies [4 weeks]
    • 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

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. 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 Access Adelaide.

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    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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  • Policies & Guidelines
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