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Optimization and State Estimation Fundamentals
Rating: 4.5 out of 5(252 ratings)
3,291 students

Optimization and State Estimation Fundamentals

Learn optimization fundamentals and state estimation techniques with this practical course!
Created byProf. Ryan Ahmed, Ph.D., MBA | 600,000+ Students | Best-Selling Instructor, Mitchell Bouchard, Stemplicity Inc.
Last updated 6/2018
English

What you'll learn

  • Understand the theory of operation of Kalman filters and optimization strategies
  • Estimate system states using Kalman Filters
  • Extract parameters from data using optimization strategies
  • Implement optimization and state estimation algorithms in MATLAB environment

Course content

2 sections • 21 lectures • 4h 36m total length

  • Introduction to State Estimation6:43
  • State Space Representation14:33
  • State Space Block diagram16:32

    Visualize state space systems with block diagrams, derive x dot = Ax + Bu and y = Cx + Du, and relate observability to state estimation via the observability matrix.

  • State Space Example18:32
  • OCV-RRC Battery Model in State Space15:52
  • OCV-RRC Battery Model in State Space - MATLAB Example23:17
  • Introduction to Kalman Filters18:01
  • Conceptional Overview of Kalman Filter12:55

    Explore the conceptual Kalman filter, fusing a priori model predictions and sensor measurements to yield an optimal corrected state estimate with quantified uncertainty.

  • Kalman Filter Theory of operation12:48

    Explain Kalman filter basics in a state-space framework, detailing covariance matrices P and Q and measurement noise R, and show how the Kalman gain fuses predictions with measurements.

  • Kalman Filter Implementation7:22
  • Extended Kalman Filter3:49
  • Extended Kalman Filter Algorithm in MATLAB12:56

    Apply an extended Kalman filter in MATLAB to estimate the battery state of charge with a combined model. Generate data, linearize the model, and implement the EKF to track charge.

  • Extended Kalman Filter Algorithm in MATLAB - Part 212:56

    Learn how to implement the extended Kalman filter in MATLAB for estimating the state of charge, using prediction and correction steps, covariance matrices, and process and measurement uncertainty.

Requirements

  • Basic Mathematics background

Description

This course covers the details of how to develop optimization and state estimation algorithms and apply them to real world practical applications. The course covers the following topics:

  1. Basic of system modeling which is how to describe any mechanical or electrical system in a mathematical form. 
  2. The theory of operation of Genetic Algorithm optimization which is extensively used in several industrial and academic applications 
  3. How to optimize parameters using experimental data
  4. Implementation of Genetic algorithm logic in MATLAB environment and apply it to real world problems
  5. How to represent systems in State space representation form. 
  6. Theory of operation of state estimation strategies such as Kalman Filtering 
  7. How to apply state estimation strategies such as Kalman filtering in MATLAB to real world problems.

Who this course is for:

  • For people who want to learn how to develop optimization/Estimation algorithms in MATLAB and Simulink
  • For students who want to learn Genetic Algorithm optimization theory and practical implementation
  • For students who want to learn Kalman filtering and state estimation strategies implementation

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