ME233 discusses advanced control methodologies and their applications to engineering systems, including but not limited to: Linear Quadratic Optimal Control, Stochastic State Estimation, Kalman Filters, Linear Quadratic Gaussian Problems, Loop Transfer Recovery, System Identification, Adaptive Control and Model Reference Adaptive Systems, Self Tuning Regulators, Repetitive Control, Disturbance Observers.

**Instructor**: Tony Kelman, Department of Mechanical Engineering, UC Berkeley.

- 5136 Etcheverry Hall, 510-859-4678, kelman AT berkeley DOT edu
- Office Hours: M 3:30 pm - 5:00 pm, F 3:30 pm - 5:00 pm

**Teaching Assistant**: Yujia Wu, Department of Mechanical Engineering, UC Berkeley.

- yujia.wu AT berkeley DOT edu
- Office Hours: W 2:00 pm - 3:00 pm, 1165 Etcheverry Hall

**Lectures**: Tu, Th 3:30 pm - 5:00 pm in 150 Goldman School of Public Policy.**Discussion Section**: W 4:00 pm - 5:00 pm in 3109 Etcheverry Hall (starting second week).

**Class Notes**: 2014 lecture notes (single pdf file); ME233 Class Reader (Parts I and II) by M. Tomizuka**Online course schedule**can be found from http://schedule.berkeley.edu/; a pdf version of the course syllabus is available here.**Important dates**:

- in-class Midterm I March 10th - one sheet of notes allowed
- in-class Midterm II April 14th - one sheet of notes allowed
- Final exam 7-10 pm (150 GSPP, same as lecture) on 5/13/2016 (Friday) - open handwritten notes

Homework 1, due February 11th end of class

Homework 2, due February 25th end of class

Homework 3, due March 8th end of class

Homework 4, due April 5th end of class

Homework 5, due April 12th end of class

Playlist of lecture videos from 2014: Youtube * iTunes U * berkeley webcast

Lecture 1-2: Introduction; Dynamic Programming and Discrete-time Linear Quadratic Optimal Control

Lecture 3: Review of Probability Theory (I)

Lecture 4: Review of Probability Theory (II)

Lecture 5: Random Vectors and Conditional Probability

Lecture 6-7: Random Vector Sequences

Lecture 8-9: Principle of Least Squares Estimation

Lecture 9-10: Stochastic State Estimation (Kalman Filter)

Lecture 11: Linear Stochastic Control (Linear Quadratic Gaussian (LQG) Problem) I

Lecture 12: Review of Stabilizability etc, Infinite Horizon LQR

Lecture 13: Stationary Kalman Filters

Lecture 14: Steady State LQG

Lecture 15: Frequency Shaped LQR

Lecture 16: Tracking Control

Lecture 17: Internal Model Principle and Repetitive Control

Lecture 18: Disturbance Observer

Lecture 19: Minimum Variance Regulator

Lecture 20: Hyperstability and Adaptive Systems

Lecture 21: Recursive Least Squares Parameter Estimation

Lecture 22: Parallel Adaptation Algorithms

Lecture 23: Parameter Convergence of Adaptation Algorithms

Lecture 24: Indirect Adaptive Control, Direct Adaptive Control

Lecture 25: Stability Analysis of Direct Adaptive Control

Lecture 26: Continuous Time Pt1: Random Vector Processes, Kalman Filters, LQG

Lecture 27: Continuous Time Pt2: Loop Transfer Recovery, Frequency Shaped LQR