Lecture
This lecture covers the Extended Kalman Predictor (EKF) algorithm, focusing on the linearized Kalman Filter (KF) for multivariable control systems. The instructor explains the idea behind using the EKF with linearized KF, the formulae involved, and the reasons for choosing Xkk-1 over kk in computing Ĉk. The lecture also discusses the challenges of estimating unknown parameters with the EKF and provides examples of predator-prey systems. Various concepts such as the optimal distributed control, stochastic LQR, and evaluating control policies are explored in detail.