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Data-Driven Control: Overview
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Classroom Contents
Data-Driven Control with Machine Learning
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- 1 Data-Driven Control: Overview
- 2 Data-Driven Control: Linear System Identification
- 3 Data-Driven Control: The Goal of Balanced Model Reduction
- 4 Data-Driven Control: Change of Variables in Control Systems
- 5 Data-Driven Control: Change of Variables in Control Systems (Correction)
- 6 Data-Driven Control: Balancing Example
- 7 Data-Driven Control: Balancing Transformation
- 8 Data-Driven Control: Balanced Truncation
- 9 Data-Driven Control: Balanced Truncation Example
- 10 Data-Driven Control: Error Bounds for Balanced Truncation
- 11 Data-Driven Control: Balanced Proper Orthogonal Decomposition
- 12 Data-Driven Control: BPOD and Output Projection
- 13 Data-Driven Control: Balanced Truncation and BPOD Example
- 14 Data-Driven Control: Eigensystem Realization Algorithm
- 15 Data-Driven Control: ERA and the Discrete-Time Impulse Response
- 16 Data-Driven Control: Eigensystem Realization Algorithm Procedure
- 17 Data-Driven Control: Balanced Models with ERA
- 18 Data-Driven Control: Observer Kalman Filter Identification
- 19 Data-Driven Control: ERA/OKID Example in Matlab
- 20 System Identification: Full-State Models with Control
- 21 System Identification: Regression Models
- 22 System Identification: Dynamic Mode Decomposition with Control
- 23 System Identification: DMD Control Example
- 24 System Identification: Koopman with Control
- 25 System Identification: Sparse Nonlinear Models with Control
- 26 Model Predictive Control
- 27 Sparse Identification of Nonlinear Dynamics for Model Predictive Control
- 28 Machine Learning Control: Overview
- 29 Machine Learning Control: Genetic Algorithms
- 30 Machine Learning Control: Tuning a PID Controller with Genetic Algorithms
- 31 Machine Learning Control: Tuning a PID Controller with Genetic Algorithms (Part 2)
- 32 Machine Learning Control: Genetic Programming
- 33 Machine Learning Control: Genetic Programming Control
- 34 Extremum Seeking Control
- 35 Extremum Seeking Control in Matlab
- 36 Extremum Seeking Control in Simulink
- 37 Extremum Seeking Control: Challenging Example
- 38 Extremum Seeking Control Applications
- 39 Reinforcement Learning: Machine Learning Meets Control Theory
- 40 Deep Reinforcement Learning: Neural Networks for Learning Control Laws
- 41 Data-driven nonlinear aeroelastic models of morphing wings for control
- 42 Overview of Deep Reinforcement Learning Methods
- 43 Reinforcement Learning Series: Overview of Methods
- 44 Model Based Reinforcement Learning: Policy Iteration, Value Iteration, and Dynamic Programming
- 45 Q-Learning: Model Free Reinforcement Learning and Temporal Difference Learning
- 46 Nonlinear Control: Hamilton Jacobi Bellman (HJB) and Dynamic Programming
