# Advanced Control Systems

## 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 metholodigies and their applications to engineering systems. Metholodigies 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.