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  • Release 2019b Highlights - MATLAB and Simulink

    2,404 views 1 day ago
    Release 2019b offers hundreds of new and updated features and functions in MATLAB® and Simulink®, along with two new products. You’ll find new capabilities for your projects – no matter what you’re working on.

    R2019b includes new updates to deep learning capabilities, automotive applications and AUTOSAR, robotics and ROS, the Simulink toolstrip, and much more.

    Check out what's new: https://bit.ly/2kMi0Cr

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    Get a free product trial: https://goo.gl/ZHFb5u
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    See What's new in MATLAB and Simulink: https://goo.gl/pgGtod

    © 2019 The MathWorks, Inc. MATLAB and Simulink are registered
    trademarks of The MathWorks, Inc.
    See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders. Show less
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  • Introduction to Machine Learning & Applied Topics Play all

    Explore the fundamentals behind machine learning, focusing on unsupervised and supervised learning. You’ll learn what each approach is, and you’ll see the differences between them. In addition, you’ll explore common machine learning techniques including clustering, classification, and regression. Lastly, you’ll walk through an example machine learning workflow that outlines key decision points in the process.
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  • Predictive Maintenance Play all

    Predictive maintenance lets you estimate the optimum time to do maintenance by predicting time to failure of a machine. This way, you can minimize downtime and maximize equipment lifetime. In this series, you’ll learn how predictive maintenance works and how it is different from other strategies such as reactive and preventive maintenance. The videos will also walk you through a workflow that will help you develop a predictive maintenance algorithm. You’ll learn about condition indicators and how you can extract them from your data to discriminate between healthy and faulty states. Machine learning models are trained using the extracted condition indicators to classify different types of faults. The videos will also help you understand different estimator models, such as survival, similarity, and degradation, that are used to estimate the remaining useful life of a machine.
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  • State Space Play all

    This series introduces control techniques built on state-space equations, the model representation of choice for modern control.

    We will provide some intuition around how to think about state variables and why this representation is so powerful. We’ll walk through a simple but effective feedback controller called pole placement, or full state feedback, and show how it is able to move the eigenvalues of your system.

    We’ll also describe the concepts of controllability and observability. Finally, we’ll look at the Linear Quadratic Regulator (LQR), a popular MIMO control technique, and show how you can use it to find optimal eigenvalue locations based on weighting criteria.
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  • Introduction to Deep Learning Play all

    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.
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  • Drone Simulation and Control Play all

    See a workflow for developing a control system that takes you from the basics of drone mechanics and to the test flight.You’ll learn about the sensors and actuators used in quadcopter control. You’ll also learn how to command a quadcopter’s four propellers in very specific ways that allow the drone to independently roll, pitch, yaw, and thrust.

    We’ll then build on that knowledge to design a control system architecture for hovering a quadcopter. That means, we’re going to figure out which states we need to feedback, how many controllers we need to build, and how those controllers interact with each other.

    We’ll review the quadcopter example in Simulink® and show how each component contributes to getting a quadcopter to hover safely. We’ll also walk through the nonlinear model of the drone and operating environment.

    Finally, by the end of this series, we’ll develop a linear model of the system and use that model to tune the PID controllers.
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  • Understanding Model Predictive Control Play all

    In this series, you'll learn how model predictive control (MPC) works, and you’ll discover the benefits of this multivariable control technique.

    MPC uses a model of the system to make predictions about the system’s future behavior. MPC solves an online optimization algorithm to find the optimal control action that drives the predicted output to the reference. MPC can handle multi-input multi-output systems that may have interactions between their inputs and outputs. It can also handle input and output constraints. MPC has preview capability; it can incorporate future reference information into the control problem to improve controller performance.

    This series also discusses MPC design parameters such as the controller sample time, prediction and control horizons, constraints, and weights. It also gives you recommendations for choosing these parameters. You'll learn about adaptive, gain-scheduled, and nonlinear MPCs, and you’ll get implementation tips to reduce the computational complexity of MPC and run it faster.

    Finally, the series demonstrates examples for designing MPC controllers in MATLAB® and Simulink®.
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  • Understanding Kalman Filters Play all

    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®.
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  • Understanding PID Control Play all

    This series provides an introduction to proportional-integral-derivative (PID) control.

    PID is just one form of feedback controller, and it can be fairly easy to understand and implement. It is the simplest type of controller that uses the past, present, and future error, and it’s these primary features that you need to satisfy most control problems. That is why PID is the most prevalent form of feedback control for a wide range of real applications.

    Often, when learning something new in control theory, it’s easy to get bogged down in the detailed mathematics of the problem. So in this series, we’re going to skip most of the math and instead focus on building a solid foundation.

    Throughout this series, you’ll learn what a PID controller is, how to modify it to make it more robust, and you’ll get an overview of tuning methods. Along the way, you’ll understand how PID controllers are used to handle practical applications like actuator saturation and the anti-windup algorithms that protect against it, sensor noise and the derivative filter that is required, and multi-loop control.
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  • Understanding Control Systems Play all

    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.
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  • Understanding Wavelets Play all

    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®.
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