Data-Driven Control with MATLAB and Simulink

Traditional control methods often face challenges in handling complex systems with unknown dynamics and disturbances, such as in aerospace systems, automated driving, robotics, and motor control applications.

Data-driven control provides a powerful solution to the challenges posed by complex systems by utilizing measured data to learn controllers. This webinar introduces various data-driven control techniques such as active disturbance rejection control (ADRC), deep-learning-based model predictive control (MPC), and reinforcement learning (RL).

We will discuss the basics of these methods, their benefits and drawbacks, and share success stories from our customers who have successfully implemented data-driven controllers in their applications. Additionally, we will showcase practical demonstrations of different data-driven control methods in various applications with MATLAB and Simulink.

Resources:
- Active Disturbance Rejection Control: https://bit.ly/3wwFQqv
- Model Predictive Control Toolbox: https://bit.ly/MPC-Toolbox
- Reinforcement Learning Toolbox: https://bit.ly/RL-Toolbox

Chapters:
00:00 Introduction
00:31 Key takeaways & agenda
2:34 Why use data-driven control?
5:24 Why use MATLAB and Simulink for data-driven control?
7:03 Active disturbance rejection control (ADRC) basics
10:14 PMSM control using ADRC
18:17 Model predictive control (MPC) basics
22:36 House heating system control using data-driven MPC
26:47 Creating AI-based reduced order models
27:47 Reinforcement learning (RL) basics
30:57 Rotary inverted pendulum control using RL
36:57 Summary and resources

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