51cg

Course Aims

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:

• 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-space 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 estimation
• experimental methods for system modelling
• basic self-tuning and adaptive control
• nonlinear control

1. 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 and state observers
• select and apply appropriate methods for digital controller design
• establish design procedures for basic optimal, self-tuning and adaptive controllers

1. 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
• design regular and optimal state feedback controllers and state observers
• 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

1. 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; 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.

More Information about Week Numbers

There are no assessments for this course.