Linear Quadratic Optimal Control: Theory and Applications

Related lectures (32)

Covers Data-Driven Control methods and applications in various fields, emphasizing robustness and performance.

Explores observability in multivariable control systems and the PBH test for system reachability.

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

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

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

Introduces multivariable control, covering system theory, linear systems, and time discretization.

Explores sparse matrices in multivariable control, emphasizing projection computation and PGD convergence.

Covers the design of estimators and controllers for multivariable systems, focusing on state estimation and disturbance rejection.

Explores the control challenge in soft robotic systems and the use of simplified models with nonlinear control theory for dynamic task execution.

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

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

Covers the computation of tracking performance and transfer functions in digital control systems.

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