ENME403-18S1 (C) Semester One 2018

Linear Systems Control and System Identification

15 points

Details:
Start Date: Monday, 19 February 2018
End Date: Sunday, 24 June 2018
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 4 March 2018
  • Without academic penalty (including no fee refund): Sunday, 20 May 2018

Description

State-space modelling, solution and analysis of state-space equations. Control systems aspects include state feedback and pole placement, state estimation and optimal control. System identification, which is complementarily related to control systems design/analysis will develop and solve linear methods of model identification and creation from data.

Modern linear systems theory is the backbone of a range of advanced dynamic systems modeling and analyses with direct industry relevance across a range of fields, including aerospace, automotive, automation and others. Course covers both control systems and optimal control, as well as system identification methods (physical models identified from data), all of which are relevant to these industries and engineering practice.

Control systems and system identification will be taught in 3 x 1 hours of lecture per week. The system identification will be taught as a separate stream.  Students must pass both parts of the course.

There will be 2 short laboratories based on modeling or mimicking the design and application of these methods in industry while recognising the needs of a large class. Thus, they will be modeling and application based labs done partly outside lab (modeling) as preparation to do the lab testing.

Practical examples and applications will be brought in for all topics.

Learning Outcomes

  • Learning Outcomes and National Qualifications Framework (NQF)

    Knowledge outcomes:
  • Modern control systems and state space modeling methods, tate space modeling of SISO and MIMO systems, natural and forced responses, state transition matrix, stability, controllability and observability
  • Understanding of stability of systems and their controllability and observability in modern automation and control
  • Modern system identification methods using time domain and state space models.

    Skills outcomes:
  • Ability to derive, interpret and solve problems using modern state space control methods for continuous time and discrete time systems
  • Ability to derive and solve system identification problems using modern state space system models and experimental data, including integral-based methods and least-squares based methods
  • Able to analyse mechanical systems for linear behaviour and stability (or instability) using modern state space modeling and methods
  • Able to solve and simulate MIMO systems including control and feedback.
  • Ability to apply and solve problems for the design of modern state space and optimal control problems.
  • Able to apply these methods to complex multi-degree of system (MIMO) systems with multiple inputs
  • Research in the development, derivation and application of principles taught to a broad spectrum of problems (design of solutions) and complex systems.

    Personal / Application attributes developed:
  • Take a mechanical system, design the equations of motion, transform solve and analyse it in the frequency domain, design feedback control for desired stability and performance, and interpret the results – The A – to – Z of design, computation, analysis and implementation for feedback control of dynamic systems.
  • Broader design, problem solving and analysis skills and experience for dynamic systems
  • Use of modern computational tools (Matlab) for design, analysis and problem solving of complex problems
  • Application of modern modeling and control or system identification methods and analysis to a wider spectrum of real-life engineering problems via laboratories, problems, case studies and projects.
  • Research capability to derive and apply principles to problems.

      • University Graduate Attributes

      This course will provide students with an opportunity to develop the Graduate Attributes specified below:

      Critically competent in a core academic discipline of their award

      Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.

Prerequisites

Restrictions

ENME433, ENEL430

Course Coordinator

Geoff Chase

Lecturers

Paul Docherty and Cong Zhou

Assessment

Assessment Due Date Percentage 
Mid term test 1 13 Mar 2018 25%
Test 2 04 May 2018 25%
Short laboratories report 10%
System ID Test 01 Jun 2018 20%
Final Exam 20%

Textbooks / Resources

Recommended Reading

Belanger; Control Engineering: A Modern Approach ; Sanders College Pub, 1995.

Dutton, Ken. , Thompson, S., Barraclough, Bill; The art of control engineering ; Addison Wesley, 1997.

Franklin et al; Digital control of dynamic systems ; 2nd Edition; Addison-Wesley, 1990.

Franklin et al; Feedback Control of Dynamic Systems ; 3rd Edition; Addison-Wesley, 1994.

Kailath, Thomas; Linear systems ; Prentice-Hall, 1980.

Ogata; Modern Control Engineering ; 2nd; Addison-Wesley, 1990.

Stefani, Raymond T. , Hostetter, G. H; Design of feedback control systems ; 3rd ed.; Saunders College Pub/Harcourt Brace Jovanovich College Pub, 1994.

Indicative Fees

Domestic fee $1,059.00

International fee $5,125.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see Mechanical Engineering .

All ENME403 Occurrences

  • ENME403-18S1 (C) Semester One 2018
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