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