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Data-Driven Control: Overview

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 Control: ERA/OKID Example in Matlab

System Identification: Full-State Models with Control

System Identification: Regression Models

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

Reinforcement Learning: Machine Learning Meets Control Theory

Deep Reinforcement Learning: Neural Networks for Learning Control Laws

Data-driven nonlinear aeroelastic models of morphing wings for control

Overview of Deep Reinforcement Learning Methods

Reinforcement Learning Series: Overview of Methods

Model Based Reinforcement Learning: Policy Iteration, Value Iteration, and Dynamic Programming

Q-Learning: Model Free Reinforcement Learning and Temporal Difference Learning

Nonlinear Control: Hamilton Jacobi Bellman (HJB) and Dynamic Programming

Description:

Explore an extensive lecture series on data-driven control and machine learning applications in control theory. Delve into topics such as linear system identification, balanced model reduction, eigensystem realization algorithm, and dynamic mode decomposition with control. Learn about advanced techniques like sparse identification of nonlinear dynamics, model predictive control, and various machine learning control methods including genetic algorithms and reinforcement learning. Gain practical insights through Matlab examples and Simulink demonstrations. Discover how to apply these concepts to real-world problems, such as tuning PID controllers and developing nonlinear aeroelastic models for morphing wings. Based on Chapters 9 & 10 from "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz, this comprehensive series provides a deep understanding of cutting-edge optimization techniques in control theory.

Data-Driven Control with Machine Learning

Steve Brunton

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#Data Science #Computer Science #Machine Learning #Programming #Domain-Specific Languages (DSL) #MATLAB #Reinforcement Learning #Engineering #Electrical Engineering #Simulink #Control Theory #Deep Reinforcement Learning

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