Module Details

Module Code: MECH9004
Title: Control System Design
Long Title: Control System Design
NFQ Level: Expert
Valid From: Semester 1 - 2009/10 ( September 2009 )
Duration: 1 Semester
Credits: 5
Field of Study: 5211 - Mechanical Engineering
Module Delivered in: 1 programme(s)
Module Description: This module builds on existing knowledge of the classical time and frequency domain approach to control system design. The emphasis is on the design of SISO and MIMO to accomplish control objectives, using modern control methods. The state-space approach is introduced as a generalized time-domain method for modelling, analyzing and designing a wide range of control systems and is particularly well suited to digital computational techniques. Matlab and simulink are used extensively to simulate the effects of various control strategies.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Design feedback control systems in the frequency domain to achieve desired system performance using phase-lead and phase-lag compensators.
LO2 Analyse the dynamic behaviour of mechanical/process systems and design full-state feedback controllers and observers using state variable models.
LO3 Evaluate and communicate the effect of various control strategies on systems performance using control simulation software such as Matlab and Simulink
LO4 Estimate the effect of model uncertainty and formulate a solution to various optimal control problems in mechanical/process systems.
LO5 Describe and explain the main approaches to intelligent control system design.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Design of feedback control systems
Several design techniques in the frequency domain to achieve the desired system performance are proposed. The powerful lead and lag controllers are introduced and used in several design examples. Phase-lead and phase-lag control design approaches using both root locus plots and Bode diagrams are presented. The proportional-integral (PI) controller is revisited in the context of achieving high steady-state tracking accuracies.
The state-space approach, State variable models
The state-space approach is a generalized time-domain method for modelling, analyzing and designing a wide range of control systems and is particularly well suited to digital computational techniques. The state-space method is applied to; (a) Multiple Input, Multiple Output (MIMO) systems, or multivariable systems (b) Non-linear and time-variant systems (c) Alternative controller design approaches. State-Transition matrix, characteristic equations, eigenvalues, eigenvectors. Controllability Canonical Form (CCF), Observability Canonical Form (OCF), Jordan Canocical form (JCF).
Design of State variable Feedback systems
System test for controllability and observability. Introduce the pole placement design technique. Ackermann’s formula to determine the state variable feedback gain matrix to place the system poles at the desired locations. The observer design process and the applicability of Ackermann’s formula. The state variable compensator is obtained by connecting the full-state feedback law to the observer. Optimal control system design and the use of internal model design to achieve prescribed steady-state response to selected input commands.
Optimal control problems
The terminal control problem, The minimum time control problem, The minimum energy control problem, the regulator control problem, the tracking control problem.
Robust Control
The design of highly accurate control systems in the presence of significant uncertainty requires the designer to seek a robust system. Five methods for robust design, including root locus, frequency response, and ITAE methods for a robust PID system. The recent design methodologies address the fact that physical systems and the external environment in which they operate cannot be modeled precisely, may change in an unpredictable manner, and may be subject to significant disturbances.
Intelligent Control Systems
System structure. The preception, cognition and actuator subsystems. Fuzzy logic control. Fuzzy sets, Fuzzy relations. Neural network control systems, network architecture, learning, back propagation. Neurofuzzy control. Overview of model-based predictive control.
Module Content & Assessment
Assessment Breakdown%
Coursework50.00%
End of Module Formal Examination50.00%

Assessments

Coursework
Assessment Type Written Report % of Total Mark 30
Timing Every Second Week Learning Outcomes 1,2,3
Assessment Description
Dynamic Simulation lab reports
Assessment Type Open-book Examination % of Total Mark 10
Timing Week 10 Learning Outcomes 1,2,3,4
Assessment Description
Design of controller using Dynamic Simulation software (Matlab)
Assessment Type Written Report % of Total Mark 10
Timing Sem End Learning Outcomes 5
Assessment Description
Essay addressing emerging intelligent control methods.
End of Module Formal Examination
Assessment Type Formal Exam % of Total Mark 50
Timing End-of-Semester Learning Outcomes 1,2,4,5
Assessment Description
End-of-Semester Final Examination
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Formal lecture Every Week 2.00 2
Lab Contact Dynamic simulation lab Every Second Week 1.00 2
Independent & Directed Learning (Non-contact) Non Contact Self directed learning Every Week 4.00 4
Total Hours 8.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
This module has no Part Time workload.
 
Module Resources
Recommended Book Resources
  • Richard C. Dorf, Robert H. Bishop. (2008), Modern Control Systems, 11/E. Prentice Hall, [ISBN: ISBN-13: 9780132270281].
  • Roland Burns. (2001), ADVANCED CONTROL ENGINEERING, Imprint: BUTTERWORTH HEINEMANN, p.464, [ISBN: ISBN-13: 978-0-7506-5100-4].
Supplementary Article/Paper Resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_EMECE_9 Master of Engineering in Mechanical Engineering 9 Elective