Advanced Control Systems II
ME233 Advanced Control Systems II, UC Berkeley, Spring 2014
I developed course notes for this course in 2014. It is not currently taught at UW but some of the materials may move to a new course.
ME233 discusses advanced control methodologies and their applications to engineering systems. Methodologies include but are not limited to: Linear Quadratic Optimal Control, Kalman Filter, Linear Quadratic Gaussian Problem, Loop Transfer Recovery, System Identification, Adaptive Control and Model Reference Adaptive Systems, Self Tuning Regulators, Repetitive Control, Disturbance Observers.
Class Notes
Lecture notes (single pdf file)
Lectures
Lecture 1: Introduction; Dynamic Programming; Discrete-time Linear Quadratic Optimal Control; Lecture video on youtube: 1 2
Lecture 2: Review of Probability Theory (I);
Lecture 3: Review of Probability Theory (II);
Lecture 4: Probability and Random Process;
Lecture 5: Principle of Least Square Estimation; Lecture video on youtube
Lecture 6: Stochastic State Estimation (Kalman Filter) I; Lecture video on youtube
Lecture 7: Stochastic State Estimation (Kalman Filter) II; Lecture video on youtube
Lecture 8: Linear Stochastic Control (Linear Quadratic Gaussian (LQG) Problem) I; Lecture video on youtube
Lecture 9: Linear Stochastic Control (Linear Quadratic Gaussian (LQG) Problem) II; Lecture video on youtube
Lecture 10: MIMO Control and Discretization;
Lecture 11: Loop Transfer Recovery I;
Lecture 12: Loop Transfer Recovery II
Lecture 13: Frequency Shaped LQ;
Lecture 14: Zero Phase Tracking Control; Preview Control;
Lecture 15: Internal Model Principle and Repetitive Control;
Lecture 16: Disturbance Observer (I);
Lecture 17: Disturbance Observer (II);
Lecture 18: System Identification;
Lecture 19: Stability of Adaptive Systems I;
Lecture 20: Stability of Adaptive Systems II;
Lecture 21: Parallel Adaptation Algorithms;
Lecture 22: Parameter convergence of adaptation algorithms
Lecture 23: Direct and Indirect Adaptive Control I
Lecture 24: Direct and Indirect Adaptive Control II
Lecture 25: Adaptive Prediction, Minimum Variance Control.