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)