Multivariable Control: State Estimation and Disturbance Rejection

Related lectures (32)

Page 3 of 4

Explores Eigenvalue Assignment in multivariable control, emphasizing the effects of discretization and the challenges in preserving system structure.

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Explores Kalman predictors and estimators in multivariable control systems.

Explores optimality in decision theory and unbiased estimation, emphasizing sufficiency, completeness, and lower bounds for risk.

Discusses the steady-state Kalman predictor, provides examples, and compares Luenberger and Kalman filtering.

Discusses closed-loop eigenvalue selection and disturbance impact in multivariable control systems.

Covers the design and analysis of multivariable control systems, focusing on stability and zero steady-state tracking error.

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

Explores exact and approximate sampling in multivariable control systems, discussing stability, eigenvalues, and system properties.

Explores Ackermann's formula and methods for eigenvalue assignment in multivariable control systems.