Multivariable Control
The basics of
- description and analysis of dynamic systems, including control systems, in continuous and discrete time using state-space methods
- digital control
- control of multivariable systems
Learning outcomes
After passing the course the student is expected to
- understand basic conceps and definitions concerning state-space models
- be able to determine a linear state-space model from another system description (e.g. nonlinear differential equation)
- understand the relationship between a state-space model and a transfer function
- be able to analyse important system properties (e.g. stability, observability, controllability) of a state-space model
- be able to determine control laws based on state feedback to satisfy given specifications (e.g. pole placement)
- understand how a discrete-time state-space model is determined from a continuous-time state-space model
- understand important concepts of discrete-time models and control laws (e.g. aliasing, ringing)
- know methods for design of multivariable control systems (e.g. decentralised control, model predictive control)
