Last modified: 31 May 2022 13:05
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.
|Session||First Sub Session||Credit Points||15 credits (7.5 ECTS credits)|
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:
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.
Information on contact teaching time is available from the course guide.
2x homework assignment (set of problems) (30% each)
Design exercise (40%)
Alternative Resit Arrangements
1x Coursework Exercise (100%)
There are no assessments for this course.
|Knowledge Level||Thinking Skill||Outcome|
|Factual||Remember||ILO’s for this course are available in the course guide.|
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