Covers the basics of multivariable control, including system modeling, temperature control, and optimal strategies, emphasizing the importance of considering all inputs and outputs simultaneously.
Focuses on designing reduced-order observers in multivariable control systems, emphasizing the importance of observers and eigenvalue assignment in controller design.
Explores the Extended Kalman Predictor algorithm and the linearized Kalman Filter for multivariable control systems, discussing the challenges and applications.
Explores offset-free tracking in multivariable control, covering necessary conditions and feedforward compensation to reject disturbances and achieve constant setpoints.
