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Last modified: 25 May 2018 11:16

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 None. Sustained Study No
  • Dr Sumeet Aphale

Qualification Prerequisites

  • Programme Level 5

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

  • Either EE3043 Control Systems (Passed) or EG3043 Control Systems (Passed)
  • One of 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
  • Any Undergraduate Programme (Studied)

What other courses must be taken with this course?


What courses cannot be taken with this course?

  • EG501T Advanced Control Engineering (Studied)

Are there a limited number of places available?


Course Description

Course Aims

To extend the work of earlier level courses in control systems to advanced, modern control methods, including transfer function, state vector and artificial-intelligence-based techniques, 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 vector 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 and parameter estimation
* experimental methods for system modeling
* basic self-tuning and adaptive control
* nonlinear control
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, state observers and parameter estimators
* select and apply appropriate methods for digital controller design
* establish design procedures for basic optimal, self-tuning and adaptive controllers
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
* apply the root locus and frequency domain methods to the design of digital controllers
* design state feedback controllers, state observers and parameter estimators
* 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
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; Modeling 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. State and Parameter Estimation - structure and design of state observers and estimators; Luenberger observer design; RLS parameter estimators.
6. 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.
7. Self-tuning and Adaptive Systems - system structures; gain scheduling controllers; self tuning controllers; model reference adaptive controllers.
8. Nonlinear systems definition; System stability analysis based on Lyapanov theory; Nonlinear controller and observer design based on nonlinear control techniques including sliding mode, feedback linearization, input-output linearization, and backstepping; Adaptive controller and observer introduction.

Contact Teaching Time

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

Teaching Breakdown

More Information about Week Numbers

Details, including assessments, may be subject to change until 31 August 2023 for 1st half-session courses and 22 December 2023 for 2nd half-session courses.

Summative Assessments

1st Attempt: 1 three hour written examination paper (60%) Assignment 1 (20%) Assignment 2 (20%)

Resit: Standard July resit is available.

Formative Assessment

Two assignments (2 x 20%)


a) Students can receive feedback on their progress with the course on request at the weekly tutorial/feedback sessions.
b) Course assignments and projects will be graded in a timely fashion and detailed feedback will be given.
c) Students requesting feedback on their exam performance should make an appointment with the course coordinator within 2 weeks of the publication of the exam results.

Course Learning Outcomes


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