Last modified: 24 Jun 2020 14:31
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 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:
B) have gained intellectual skills so that they are able to:
C) have gained practical skills so that they are able to:
D) have gained or improved transferable skills so that they are able to:
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.
Information on contact teaching time is available from the course guide.
2x homework assignment (set of problems) (30% each)
Design exercise (40%)
There are no assessments for this course.
|Knowledge Level||Thinking Skill||Outcome|