 ## ME424-F22 Modern Control and Estimation

• Dec , 2022
ME424 Modern Control and Estimation

### Course Information

Instructor:      Wei Zhang (zhangw3@sustech.edu.cn)
Time:               Wednesday 16:20-18:10 / Friday 10:20-12:10 (even week)
Location:        三教 210
TAs:                 Haoxiang Luo, Xiaoxiang Liu, Yongjian Su
Recordings:    https://space.bilibili.com/474380277/channel/seriesdetail?sid=291615

### Description

This course will introduce the students to the fundamental concepts and methods in modern control and estimation theory. Topics include state-space modeling of dynamical systems, least-square estimation and system identification, state-feedback and output-feedback controller design, observer design, linear quadratic regulators, and Kalman filter. The course will also connect these control and estimation methods to applications in robotics, mechanical, electrical, and aerospace systems.

### Lecture Notes

• Lecture 1: Linear Algebra Review [PDF]

Linear independence, Vector space, Solution space of Ax = b, Inner product, Simple geometric sets, Quadratic sets

•  Lecture 2: State Space Models [PDF]
• State space models, From continuous to discrete time model, From nonlinear to linear model, Transfer function

•  Lecture 3: Least Squares and Basic System Identification [PDF]
• Least squares problem formulation, Solution to linear least square problems, Applications to System ID, Nonlinear least squares

•  Lecture 4: Stability, Controllability, and Observability [PDF]
• State space solutions, Internal stability, Controllability, Observability, Invariance under similarity transformation

•  Lecture 5: State-Feedback and Output-Feedback Control [PDF]
• Full State-feedback: Eigenvalue Assignment, Luenberger Observer Design, Output-feedback Control and Separation Principle

•  Lecture 6: Control Design and Testing in Drake with Python [PDF]
• Short Introduction to Drake, Observer and Controller Design, From Regulation to Tracking Control, Examples

•  Lecture 7: Kalman Filter-Probability Review [PDF]
• Probability and Conditional Probability, Random Variables and Random Vectors, Conditional Expectation, Covariance Matrix

•  Lecture 8: Kalman Filter-Derivations and Algorithm [PDF]
• Minimum Mean Squared Estimation (MMSE), Gaussian Random Vectors, Kalman Filter Derivation, Kalman Filter Implementation

•  Lecture 9: Extended Kalman Filter [PDF]
• Extended Kalman Filter Derivation, Application Examples, Implementation

•  Lecture 10: Linear Quadratic Regulator [PDF]
• General Discrete-Time Optimal Control Problem, Dynamic Programming, Linear Quadratic Regulator Derivation and Implementation

### Tutorials

• Tutorial 1: Python, Numpy and Matplotlib [file]
• Tutorial 2: Install Linux on Windows with WSL [PDF]
• Tutorial 3: Install Drake on Your Computer [PDF]