Overview

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.

Syllabus

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.