Data-Driven Control Using MATLAB and Simulink
Overview
Date-driven control is a category of advanced control strategies that leverage machine learning techniques to improve the performance and adaptability of control systems. Reinforcement Learning (RL) and model predictive control (MPC) are two of the most popular data-driven control techniques which have the potential to solve tough decision-making problems in many applications, including industrial automation, process control, aerospace, autonomous driving, and robotics. In this session, you will learn what these technologies are, how they compare to each other, and how to implement them using MathWorks tools.
Highlights
- What are reinforcement learning and model predictive control, and how do they work?
- When should you use reinforcement learning or model predictive control as opposed to more traditional control techniques?
- Example: Designing an office building energy management system using RL and MPC approaches
About the Presenter
Jordan Olson is an Application Engineer specializing in advanced and data-driven control techniques. He joined MathWorks in 2022 and supports a variety of industries, including aerospace and defense, automotive, energy production, and wireless communications. Jordan holds two master's degrees, one in Mechanical Engineering and the other in Electrical Engineering, both from The University of Alabama. During his graduate studies, Jordan focused on advanced control techniques in automotive applications, specifically hybrid-electric powertrain control and autonomous vehicles.
Product Focus
This event is part of a series of related topics. View the full list of events in this series.
