Explores the Extended Kalman Predictor algorithm and the linearized Kalman Filter for multivariable control systems, discussing the challenges and applications.
Covers the basics of multivariable control, including system modeling, temperature control, and optimal strategies, emphasizing the importance of considering all inputs and outputs simultaneously.
Explores the time-varying Kalman filter, state estimation, challenges in conditioning on measured outputs, and the importance of affine transformations.
Focuses on designing reduced-order observers in multivariable control systems, emphasizing the importance of observers and eigenvalue assignment in controller design.
