Data-Driven Control With Machine Learning

About Course

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.

Course Content

Data-Driven Control with Machine Learning

Data-Driven Control: Overview

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System Identification: Dynamic Mode Decomposition with Control

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System Identification: DMD Control Example

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System Identification: Koopman with Control

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System Identification: Sparse Nonlinear Models with Control

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Model Predictive Control

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Sparse Identification of Nonlinear Dynamics for Model Predictive Control

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Machine Learning Control: Overview

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Machine Learning Control: Genetic Algorithms

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Machine Learning Control: Tuning a PID Controller with Genetic Algorithms

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Machine Learning Control: Tuning a PID Controller with Genetic Algorithms (Part 2)

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Machine Learning Control: Genetic Programming

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Machine Learning Control: Genetic Programming Control

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Extremum Seeking Control

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Extremum Seeking Control in Matlab

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Extremum Seeking Control in Simulink

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Extremum Seeking Control: Challenging Example

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Extremum Seeking Control Applications

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System Identification: Regression Models

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System Identification: Full-State Models with Control

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Data-Driven Control: ERA/OKID Example in Matlab

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Data-Driven Control: Linear System Identification

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Data-Driven Control: The Goal of Balanced Model Reduction

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Data-Driven Control: Change of Variables in Control Systems

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Data-Driven Control: Change of Variables in Control Systems (Correction)

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Data-Driven Control: Balancing Example

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Data-Driven Control: Balancing Transformation

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Data-Driven Control: Balanced Truncation

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Data-Driven Control: Balanced Truncation Example

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Data-Driven Control: Error Bounds for Balanced Truncation

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Data-Driven Control: Balanced Proper Orthogonal Decomposition

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Data-Driven Control: BPOD and Output Projection

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Data-Driven Control: Balanced Truncation and BPOD Example

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Data-Driven Control: Eigensystem Realization Algorithm

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Data-Driven Control: ERA and the Discrete-Time Impulse Response

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Data-Driven Control: Eigensystem Realization Algorithm Procedure

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Data-Driven Control: Balanced Models with ERA

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Data-Driven Control: Observer Kalman Filter Identification

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Data-driven nonlinear aeroelastic models of morphing wings for control

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About Course

Data-Driven Control with Machine Learning

What Will You Learn?

Course Content

Data-Driven Control with Machine Learning

Data-Driven Control: Overview

System Identification: Dynamic Mode Decomposition with Control

System Identification: DMD Control Example

System Identification: Koopman with Control

System Identification: Sparse Nonlinear Models with Control

Model Predictive Control

Sparse Identification of Nonlinear Dynamics for Model Predictive Control

Machine Learning Control: Overview

Machine Learning Control: Genetic Algorithms

Machine Learning Control: Tuning a PID Controller with Genetic Algorithms

Machine Learning Control: Tuning a PID Controller with Genetic Algorithms (Part 2)

Machine Learning Control: Genetic Programming

Machine Learning Control: Genetic Programming Control

Extremum Seeking Control

Extremum Seeking Control in Matlab

Extremum Seeking Control in Simulink

Extremum Seeking Control: Challenging Example

Extremum Seeking Control Applications

System Identification: Regression Models

System Identification: Full-State Models with Control

Data-Driven Control: ERA/OKID Example in Matlab

Data-Driven Control: Linear System Identification

Data-Driven Control: The Goal of Balanced Model Reduction

Data-Driven Control: Change of Variables in Control Systems

Data-Driven Control: Change of Variables in Control Systems (Correction)

Data-Driven Control: Balancing Example

Data-Driven Control: Balancing Transformation

Data-Driven Control: Balanced Truncation

Data-Driven Control: Balanced Truncation Example

Data-Driven Control: Error Bounds for Balanced Truncation

Data-Driven Control: Balanced Proper Orthogonal Decomposition

Data-Driven Control: BPOD and Output Projection

Data-Driven Control: Balanced Truncation and BPOD Example

Data-Driven Control: Eigensystem Realization Algorithm

Data-Driven Control: ERA and the Discrete-Time Impulse Response

Data-Driven Control: Eigensystem Realization Algorithm Procedure

Data-Driven Control: Balanced Models with ERA

Data-Driven Control: Observer Kalman Filter Identification

Data-driven nonlinear aeroelastic models of morphing wings for control

Student Ratings & Reviews