Data-Driven Control with Machine Learning

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Data-Driven Control with Machine Learning

Data-Driven Control discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science.

It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy.

Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state-of-the-art.

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What Will You Learn?

  • Learn Data-Driven Control with Machine Learning from A to Z

Course Content

Data-Driven Control with Machine Learning

  • Data-Driven Control: Overview
    00:00
  • Data-Driven Control: Linear System Identification
    00:00
  • Data-Driven Control: The Goal of Balanced Model Reduction
    00:00
  • Data-Driven Control: Change of Variables in Control Systems
    00:00
  • Data-Driven Control: Change of Variables in Control Systems (Correction)
    00:00
  • Data-Driven Control: Balancing Example
    00:00
  • Data-Driven Control: Balancing Transformation
    00:00
  • Data-Driven Control: Balanced Truncation
    00:00
  • Data-Driven Control: Balanced Truncation Example
    00:00
  • Data-Driven Control: Error Bounds for Balanced Truncation
    00:00
  • Data-Driven Control: Balanced Proper Orthogonal Decomposition
    00:00
  • Data-Driven Control: BPOD and Output Projection
    00:00
  • Data-Driven Control: Balanced Truncation and BPOD Example
    00:00
  • Data-Driven Control: Eigensystem Realization Algorithm
    00:00
  • Data-Driven Control: ERA and the Discrete-Time Impulse Response
    00:00
  • Data-Driven Control: Eigensystem Realization Algorithm Procedure
    00:00
  • Data-Driven Control: Balanced Models with ERA
    00:00
  • Data-Driven Control: Observer Kalman Filter Identification
    00:00
  • Data-Driven Control: ERA/OKID Example in Matlab
    00:00
  • System Identification: Full-State Models with Control
    00:00
  • System Identification: Regression Models
    00:00
  • System Identification: Dynamic Mode Decomposition with Control
    00:00
  • System Identification: DMD Control Example
    00:00
  • System Identification: Koopman with Control
    00:00
  • System Identification: Sparse Nonlinear Models with Control
    00:00
  • Model Predictive Control
    00:00
  • Sparse Identification of Nonlinear Dynamics for Model Predictive Control
    00:00
  • Machine Learning Control: Overview
    00:00
  • Machine Learning Control: Genetic Algorithms
    00:00
  • Machine Learning Control: Tuning a PID Controller with Genetic Algorithms
    00:00
  • Machine Learning Control: Tuning a PID Controller with Genetic Algorithms (Part 2)
    00:00
  • Machine Learning Control: Genetic Programming
    00:00
  • Machine Learning Control: Genetic Programming Control
    00:00
  • Extremum Seeking Control
    00:00
  • Extremum Seeking Control in Matlab
    00:00
  • Extremum Seeking Control in Simulink
    00:00
  • Extremum Seeking Control: Challenging Example
    00:00
  • Extremum Seeking Control Applications
    00:00
  • Data-driven nonlinear aeroelastic models of morphing wings for control
    00:00

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