ACS317 State-Space Control Design
Module Description (subject to change)
The aims of this modules are: to introduce state-space methods for the analysis and design of controllers for multivariable systems; to teach the use of analytical tools and methods for state-space control design; to demonstrate similarities between continuous and sampled data systems; and to extend the analysis to non-linear systems.
Material to be covered includes: Structural properties (modal decomposition, reachability, observability, stability); design (pole assignment, observer design, separation principle, internal model principle, optimal control, LQG, reference tracking, integral control) of continuous systems and equivalents for sampled-data systems.
Credits: 10 (Autumn semester)
Pre-requisites: ACS230 (Control systems design and analysis)
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).
By the end of the module students will be able to:
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.
Introduction to state-space: State-space description of multivariable physical systems; continuous-time and sampled-data systems; Linear state-space description; Canonical state-space transformations; Linearisation and equilibrium points.
Analysis: Solution of state-space equations; State-transition matrix; Discretisation of continuous-time systems; Modal decomposition; Transfer functions of state-space systems. Structural Properties. Controllability; Observability; Stability; Minimal realisation; Stabilizability; Detectability.
Controller Design: State-feedback pole assignment; Linear Quadratic Regulator (Optimal control); Reference tracking, Integral control (state augmentation); Internal model principle.
Observer Design: Pole assignment; Separation principle; Kalman filter (Optimal estimation).
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: 18 hours
Learning and Teaching Materials
All teaching materials will be available via MOLE.
Formal exam (90%) 2 hrs in Autumn exam period.
4x Labs (2.5% each)
No resit examination is available for this module.
There are a number of opportunities for feedback in this module.
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.