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EE501T: ADVANCED CONTROL ENGINEERING (2021-2022)

Last modified: 31 Aug 2021 09:31


Course Overview

This is the second course in control engineering which looks at the state-space representation of systems as well as state-space based control design techniques.  The course also introduces basic concepts in System Identification and Nonlinear Control.  Traditional continuous-time as well as sampled-data (digital) systems are covered.


Course Details

Study Type Undergraduate Level 5
Session First Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • Dr Sumeet S Aphale

What courses & programmes must have been taken before this course?

  • One of EE3043 Control Systems (Passed) or EG3043 Control Systems (Passed) or Master Of Science In Industrial Robotics
  • One of Programme Level 5 or Master Of Science In Industrial Robotics or Master of Engineering in Electrical & Electronic Engineering or Master Of Engineering In Elec & Electronic Eng W Renewabl En or Master of Engineering in Mechanical & Electrical Eng

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

  • EG501T Advanced Control Engineering (Studied)

Are there a limited number of places available?

No

Course Description

Course Aims

To extend the work of the third-year courses in control systems to advanced, modern control methods, mainly focusing on the state-space approach, applicable to the design of both continuous and discrete-time systems.

Main Learning Outcomes

By the end of the course students should: A) have knowledge and understanding of:

  • classification of control systems
  • the basic characteristics of discrete and hybrid control systems
  • the effects of sampling on system behaviour
  • design methods for discrete and hybrid systems
  • various forms of state-space models for control systems and their use
  • the analysis methods in state-space like: stability, controllability, observability, state feedback control, pole placement
  • the characteristics and design of optimal controllers the concepts of state estimation
  • experimental methods for system modelling
  • basic self-tuning and adaptive control
  • nonlinear control
  1. B) have gained intellectual skills so that they are able to:
  • select and apply methods for investigating the stability of continuous and discrete-time control systems
  • select appropriate structures for feedback controllers and state observers
  • select and apply appropriate methods for digital controller design
  • establish design procedures for basic optimal, self-tuning and adaptive controllers
  1. C) have gained practical skills so that they are able to:
  • set up, manipulate and use state-space, block diagram and transfer function representations of systems
  • investigate stability, controllability and observability
  • select an appropriate sample rate for the implementation of digital control
  • design regular and optimal state feedback controllers and state observers
  • devise appropriate experimental procedures and establish system models from time domain and frequency domain response data
  • design basic optimal, self-tuning and adaptive controllers
  • use Matlab-based software for control system analysis and design
  1. D) have gained or improved transferable skills so that they are able to:
  • use WWW-based material to aid learning
  • engage in discussion regarding problem solving methodology
  • devise checking procedures
  • be flexible and multi-faceted in their thinking

Course Content 1. Introduction - system classification; continuous, discrete-time and hybrid systems; linear and non-linear systems; time invariant and time varying systems. Course philosophy.

2. State-space modelling - solution of the state equation; Modelling in state space (electrical and mechanical systems), conversion between transfer function and state-space, eigenvalues and stability; eigenvectors and state matrix sensitivity, controllability and observability;

3. State-space control design - pole placement approach to state feedback design; output feedback, optimal control and Linear Quadratic Regulator; extension to non-linear systems.

4. System Identification - review of types and selection of system models; transfer function and state vector models; impulse and frequency response testing; time and frequency domain methods for identification from experimental data.

5. Discrete and Digital Control - mixed continuous and discrete time systems; z-transformation, z-domain transfer function and state-space model; system response and stability; stability analysis using Routh-Hurwitz; controller design using root locus; discrete approximations, frequency domain and direct methods.

6. Self-tuning and Adaptive Systems - system structures; gain scheduling controllers; self tuning controllers; model reference adaptive controllers.

7. Nonlinear systems – Description and behaviour, System stability analysis based on Lyapunov theory; describing functions, phase portraits, system linearization, Introduction to nonlinear control techniques including feedback linearization and input-output linearization.


In light of Covid-19 this information is indicative and may be subject to change.

Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

  • 2 Seminars during University weeks 9 - 18
  • 1 Tutorial during University weeks 10 - 18, 10 - 18

More Information about Week Numbers


In light of Covid-19 and the move to blended learning delivery the assessment information advertised for second half-session courses may be subject to change. All updates for second-half session courses will be actioned in advance of the second half-session teaching starting. Please check back regularly for updates.

Summative Assessments

First Attempt

2x homework assignment (set of problems) (30% each)

Design exercise (40%)

Alternative Resit Arrangements

1x Coursework Exercise (100%)

Formative Assessment

There are no assessments for this course.

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
FactualRememberILO’s for this course are available in the course guide.

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