ACS6129 Modern Control & Systems Identification

Module Description (subject to change)

This module introduces students to advanced state-space control systems analysis and design methods for multivariable systems. The focus is linear time-invariant (LTI) systems in the continuous-time domain, although an introduction is also provided to discrete-time cases and nonlinear cases. Students will also be introduced to system identification techniques. System identification uses observations of inputs and outputs from physical systems and estimates dynamical models directly. The theoretical framework and the computational algorithms are explored using synthetic and real problems to show how models can be estimated and validated for future use.

Credits: 15 (Autumn semester)

Module Leader

LZG Profile Picture










Dr Lingzhong Guo

Email: l.guo@shef.ac.uk
Pam Liversedge Building

If you have any questions about the module please talk to me during the lectures or the labs in the first instance. It is likely that other students will learn from any questions you ask as well, so don’t be afraid to ask questions.

Outside of lectures please contact me via email during normal working hours (9am-5pm Monday - Friday).

Other Teaching Staff

Professor Visakan Kadirkamanathan
Email: visakan@sheffield.ac.uk

Learning Outcomes

Learning Outcomes

By the end of the module students will be able to:

  • Understand the state-space representation of a system and apply state-space methods to model and analyse systems. [SM1fl, SM2fl, EA1fl]
  • Develop appropriate linearised models of non-linear systems, and explain their limitations in the context of controller design. [SM1fl, SM3fl, EA1fl]
  • Develop discrete-time models of continuous-time systems and explain the effect of sample rate on closed-loop system stability and performance. [SM1fl, SM3fl, EA1fl, EP3fl]
  • Employ computational methods to perform the analysis, simulation and control system synthesis of systems modelled in state-space form. [EA3m, EA4p]
  • Explain the process of system identification and the requirements placed on the signals and systems involved. [SM1fl, SM3fl]
  • Explain and apply suitable theoretical tools for the evaluation of systems identification techniques and the resulting identified models. [EA1fl]
  • Design and implement suitable experiments for system identification and interpret results in a critical way. [EA3fl, D1fl]

This module satisfies the AHEP3 (Accreditation of Higher Education Programmes, Third Edition) Learning Outcomes that are listed in brackets after each learning outcome above. For further details on AHEP3 Learning Outcomes, see the downloads section of our accreditation webpage.

Syllabus

Syllabus

TBC

Teaching Methods

Learning and Teaching Methods

NOTE: This summary of teaching methods is representative of a normal Semester. Owing to the ongoing disruption from Covid-19, the exact method of delivery will be different in 2020/21.

Lectures: 30 hours
Tutorials: 9 hours
Labs: 12 hours
Independent Study: 99 hours

Teaching Materials

Learning and Teaching Materials

All teaching materials will be available via Blackboard (MOLE).

Assessment

Assessment

Coursework (Asynchronous, limited window) - 80%

Coursework (lab exercises) - 20%

Feedback

Feedback

The tutorial hours will provide student opportunities for face-to-face feedback about the modules and their learning. Students will also receive feedback on their coursework.

Student Evaluation

Student Evaluation

Students are encouraged to provide feedback during the module direct to the lecturer. Students will also have the opportunity to provide formal feedback via the Faculty of Engineering Student Evaluation Survey at the end of the module.

You can view the latest Department response to the survey feedback here.

Recommended Reading

Recommended Reading

Core texts:

  • TBC

Recommended texts:

  • TBC

Additional texts:

  • TBC