Artificial Intelligence

Apply machine learning and deep learning

Data-Driven Control with MATLAB and Simulink

The following blog post is from Melda Ulusoy, Technical Marketing Manager at MathWorks.
One of artificial intelligence (AI)’s first big successes was solving image classification problems with deep learning. AI has since been used in many other areas, including control systems. In this blog post, we will present an overview of AI for controls, highlight advantages of using MATLAB and Simulink for data-driven control, and provide details on how to register for an upcoming webinar.

AI for Control Algorithms

Feedback control algorithms are used in advanced robots, electric motors, batteries, power converters, power grids, and autonomous vehicles that drive, fly, and sail. Traditionally, feedback control algorithms relied on linear models of the machines and devices, for which a control system engineer needed to develop a control algorithm.
As control engineers strive to improve the performance of control algorithms, they are increasingly turning to techniques that can enhance performance by considering the nonlinear dynamics of the systems to control.  AI techniques are great for creating accurate nonlinear models from data. Additionally, AI is very useful when control algorithms do not rely on a model of a system, but instead learn directly from data. So, control engineers are increasingly interested in applying AI to their work.
In this post, we cover a wide set of techniques for data-driven control. These techniques use system data to either learn a model of the system or directly learn control system parameters from data.  Some of the data-driven control techniques are AI-based algorithms, while others use non-AI-based algorithms to take advantage of system data.

Why MATLAB and Simulink for Data-Driven Control?

With MATLAB and Simulink, you can design and implement a variety of data-driven controllers including extremum seeking control (ESC), active disturbance rejection control (ADRC), model reference adaptive control (MRAC), data-driven model predictive control (MPC), and reinforcement learning (RL). This is not an exhaustive list, but rather a sample of somewhat recent capabilities that have been added to the data-driven control area.
Application areas of AI and data-driven control include ESC, ADRC, MRAC, MPC, and RL.
Figure: Sample of recent capabilities in the data-driven control area.
Using MATLAB and Simulink for data-driven control comes with several advantages. Engineers can:
  • Design, test, and compare a variety of control algorithms including both traditional and data-driven techniques in a single environment.
  • Implement and test data-driven control algorithms in Simulink using pre-built Simulink blocks.
  • Automatically generate code from the control algorithm for deployment to embedded hardware.
  • Use reference examples for flight control, robotics, energy management, and other applications to quickly get started with the implementation of data-driven control algorithms.

Watch Recent Webinar

This has been a short intro to data-driven control. For a deeper dive, watch the recent webinar on Data-Driven Control with MATLAB and Simulink.
In the webinar, you will learn the basics of ADRC, MPC, and RL, and see the following demonstrations:
  • Demo 1: Permanent magnet synchronous motor (PMSM) control using ADRC (learn more in this video)
  • Demo 2: House heating system control using data-driven MPC (learn more in this video)
  • Demo 3: Rotary inverted pendulum control using RL
Representations of demos presented in the Data-Driven Control webinar
Figure: Demos presented at Data-Driven Controls webinar.
  • print


要发表评论,请点击 此处 登录到您的 MathWorks 帐户或创建一个新帐户。