State space methods (single-input single-output) • State space models and transfer functions, state feedback
• Coordinate basis, similarity transformations
• Solutions of state equations, matrix exponentials, Caley-Hamilton Theorem
• Controllability and pole placement
• State estimation, observability, Kalman decomposition
• Observer-based state feedback control, reference tracking
• Transmission zeros
• Optimal pole placement, symmetric root locus
Multi-input multi-output systems
• Transfer function matrices, state space models of multivariable systems, Gilbert realization
• Poles and zeros of multivariable systems, minimal realization
• Closed-loop stability
• Pole placement for multivariable systems, LQR design, Kalman filter
Digital Control
• Discrete-time systems: difference equations and z-transform
• Discrete-time state space models, sampled data systems, poles and zeros
• Frequency response of sampled data systems, choice of sampling rate
System identification and model order reduction
• Least squares estimation, ARX models, persistent excitation
• Identification of state space models, subspace identification
• Balanced realization and model order reduction
Case study
• Modelling and multivariable control of a process evaporator using Matlab and Simulink
Software tools
• Matlab/Simulink |
