Multivariable Control: Weight Design and Stability Analysis

Related lectures (32)

Page 3 of 4

Covers the design and implementation of state observers and output-feedback controllers in multivariable control systems.

Covers Gaussian random variables, affine transformations, and linear systems driven by Gaussian noise in multivariable control.

Covers state representation, controller design, LQR controllers, and optimal control criteria.

Explores stability, sensitivity, tracking performance, and regulation error in control systems.

Explores stability and eigenvalues of LTI systems, reachability, and controllability of dynamical systems.

Explores learning and control for complex systems, addressing challenges and opportunities in technology and interdisciplinary research.

Covers the principles of feedback control systems and their applications in various fields.

Discusses the design of weighting matrices for LQR and Gaussian random vectors.

Covers the structure theorem for state-space representations and companion forms.

Covers the design of reduced-order observers and output feedback controllers in multivariable control systems.