MATLAB

Watch this series of MATLAB Tech Talks to explore key deep learning concepts. Learn to identify when to use deep learning, discover what approaches are suitable for your application, and explore some of the challenges you might encounter.

Learn the basic concepts behind controls systems. Walk through everyday examples that outline fundamental ideas, and explore open-loop and feedback control systems.

These videos explore open-loop systems that are found in everyday appliances like toasters or showers. The series illustrates how you can tune these systems using trial-and-error to achieve a desired output. You’ll also learn about situations where open-loop control may fail due to unexpected environmental changes (disturbances) or variations in the system.

Next, you’ll explore the working principles behind feedback control, and discover how it deals with the shortcomings of open-loop control. Basic components of a feedback control system (such as “plants,” “actuators,” and “sensors”) are discussed, along with how these components interact with each other to form a closed-loop control system. You’ll discover how disturbances acting on the plant can affect system output in an undesired way, and how feedback control can compensate for such disturbances. The video series also discusses how noise can enter the system through measurement, which affects the measured output.

Finally, you’ll learn to use MATLAB and Simulink to model and simulate some of the open-loop and feedback control systems introduced in this series.

Discover real-world situations in which you can use Kalman filters. Kalman filters are often used to optimally estimate the internal states of a system in the presence of uncertain and indirect measurements. Learn the working principles behind Kalman filters by watching some introductory examples.
You will explore situations where Kalman filters are commonly used. When the states of a system can only be measured indirectly, then Kalman filter can be used to optimally estimate the states of the system. And when measurements from different sensors are available but subject to noise, Kalman filter is used to combine sensory data from various sources (known as sensor fusion) to find the best estimate of the parameter of interest.
You will also learn about state observers by walking through some examples and simple math. This will help you understand what a Kalman filter is and how it works. At a high level, Kalman filters are a type of optimal state estimator. The videos include a discussion of nonlinear state estimators, such as extended and unscented Kalman filters.
Finally, an example demonstrates how the states of a linear system can be estimated using Kalman filters, MATLAB®, and Simulink®.

MATLAB Tech Talks on Bode plot.

Watch the videos in this series to learn the fundamental concepts of state machines. A state machine is a model that describes the behavior of a system in each state. It defines how the system should transition between these states when certain conditions are true. State machines are used to model logic in many dynamic systems such as automobiles, aircraft, robots, and mobile phones.

Watch the videos in this series to learn the basics behind applications such as wavelet-based denoising and compression. You will learn fundamental concepts in wavelet analysis such as what wavelets are and how to scale and shift them. You will get an overview of the continuous and discrete wavelet transforms, and you will also see example applications of how to use these transforms in MATLAB®.

The best and brightest engineers and scientists in the world use MATLAB® and Simulink® as tools of inspiration and innovation. The result: solutions that are transforming the way we live, work, and learn.

Watch MathWorks Engineer Terry Denery demonstrate the use of simulation in the development of mechatronic systems. Although robotics is the iconic example of a mechatronic system, in reality everything from toasters and vacuum cleaners to airplanes, space craft, and automobiles fall into this category. As the name implies, mechatronic systems are developed by a team that has a broad skill set in mechanics, electronics, controls, and software. In this series, you will see how MATLAB® and Simulink® work together as a simulation platform that represents each of these engineering domains. You will discover the value that MATLAB and Simulink have in addressing several interdisciplinary design challenges, such as selecting and controlling electric motors that power the precise motion of mechanical systems. Several of the videos demonstrate rapid deployment of models to real hardware. This illustrates how to use quick tests in order to get important checks on your simulations.