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Data-Driven Control with Machine Learning

  • Course level: Intermediate

Description

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 I Learn?

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

Topics for this course

39 Lessons

Data-Driven Control with Machine Learning

Data-Driven Control: Overview00:00:00
Data-Driven Control: Linear System Identification00:00:00
Data-Driven Control: The Goal of Balanced Model Reduction00:00:00
Data-Driven Control: Change of Variables in Control Systems00:00:00
Data-Driven Control: Change of Variables in Control Systems (Correction)00:00:00
Data-Driven Control: Balancing Example00:00:00
Data-Driven Control: Balancing Transformation00:00:00
Data-Driven Control: Balanced Truncation00:00:00
Data-Driven Control: Balanced Truncation Example00:00:00
Data-Driven Control: Error Bounds for Balanced Truncation00:00:00
Data-Driven Control: Balanced Proper Orthogonal Decomposition00:00:00
Data-Driven Control: BPOD and Output Projection00:00:00
Data-Driven Control: Balanced Truncation and BPOD Example00:00:00
Data-Driven Control: Eigensystem Realization Algorithm00:00:00
Data-Driven Control: ERA and the Discrete-Time Impulse Response00:00:00
Data-Driven Control: Eigensystem Realization Algorithm Procedure00:00:00
Data-Driven Control: Balanced Models with ERA00:00:00
Data-Driven Control: Observer Kalman Filter Identification00:00:00
Data-Driven Control: ERA/OKID Example in Matlab00:00:00
System Identification: Full-State Models with Control00:00:00
System Identification: Regression Models00:00:00
System Identification: Dynamic Mode Decomposition with Control00:00:00
System Identification: DMD Control Example00:00:00
System Identification: Koopman with Control00:00:00
System Identification: Sparse Nonlinear Models with Control00:00:00
Model Predictive Control00:00:00
Sparse Identification of Nonlinear Dynamics for Model Predictive Control00:00:00
Machine Learning Control: Overview00:00:00
Machine Learning Control: Genetic Algorithms00:00:00
Machine Learning Control: Tuning a PID Controller with Genetic Algorithms00:00:00
Machine Learning Control: Tuning a PID Controller with Genetic Algorithms (Part 2)00:00:00
Machine Learning Control: Genetic Programming00:00:00
Machine Learning Control: Genetic Programming Control00:00:00
Extremum Seeking Control00:00:00
Extremum Seeking Control in Matlab00:00:00
Extremum Seeking Control in Simulink00:00:00
Extremum Seeking Control: Challenging Example00:00:00
Extremum Seeking Control Applications00:00:00
Data-driven nonlinear aeroelastic models of morphing wings for control00:00:00
Data-Driven Control with Machine Learning
Free

Enrolment validity: Lifetime

Requirements

  • Non