ME424-F22 Modern Control and Estimation

  • Dec , 2022
ME424 Modern Control and Estimation

Course Information

Instructor:      Wei Zhang (
Time:               Wednesday 16:20-18:10 / Friday 10:20-12:10 (even week)
Location:        三教 210
TAs:                 Haoxiang Luo, Xiaoxiang Liu, Yongjian Su


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


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

Homework and Projects


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