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1.Matlab/Simulink-commonly used blocks Constant & Product Blocks
 
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Please watch: "Part-2 Design and simulatio of 3 phase half wave controlled rectifier" https://www.youtube.com/watch?v=f8eQKZBV-io --~-- This video shows how to work with constant and product blocks in matlab/simulink software
Views: 722 Nageswar J
Introduction to Simulink - Webinar
 
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Get introduced to Simulink in this webinar for beginners. Get a Free Trial: https://goo.gl/C2Y9A5 Get Simulink Training: https://goo.gl/V5AH3q Explore Simulink, an environment for multidomain simulation and Model-Based Design for dynamic and embedded systems. Through product demonstrations, you will see a high-level overview of the major capabilities and how you can use Simulink to design, simulate, implement, and test a variety of time-varying systems, including communications, controls, signal processing, video processing, and image processing. About the Presenters: Ryan Gordon has over 4 years of experience with MATLAB and Simulink. Prior to joining MathWorks Ryan developed guidance and control systems for unmanned aircraft. Michael Carone is a senior product marketing manager for the Simulink platform. Michael’s background is in mechanical engineering and engineering design processes.
Views: 159468 MATLAB
Simulation Testing in Model-Based Design
 
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Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 For More on Simulation-based Tests and Simulink Test, visit: https://www.mathworks.com/products/simulink-test/ Experience a new way to perform simulation testing of your Simulink model and generated code. The recently introduced Simulink Test product provides an automation framework for early testing of Simulink models, and for reusing your model testing assets in the verification of generated code. In addition, Simulink Test removes the burden of creating custom test environments by providing features for managing test harnesses and test cases as well as for evaluating results. Webinar highlights include: Learning how to perform functional tests, execute unit/regression tests, and back-to-back tests (SIL/PIL) An end-to-end demonstration of simulation testing from requirements, design model, to code Testing strategies to create and manage reusable testing assets, artifacts, and reports
Views: 16073 MATLAB
matlab license activate renew simulink activation error problem mathworks fixed checkout failed
 
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matlab, license, error, problem, renew, activate, mathworks, fixed, matlab license ,checkout, failed, simulink,r2007, r2008, r2009, r2010,r2011, r2012,r2013,r2014,r2015,r2016,r2017,a, b ,c, 6 , 7, 8, 9, 10, 11, 12, 13, 14 , 15, 16, 17,
Views: 16787 Lord of Vel
MATLAB Anytime, Anywhere
 
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Learn about the MATLAB® family of products and their interaction, from MATLAB Online™ to MATLAB Mobile™. MATLAB Mobile: http://bit.ly/2Jrd8hq MATLAB Online: http://bit.ly/2JnKKwr MATLAB Drive™ is online storage on the cloud that allows you to sync your work from one device to another so you can access your files from anywhere. For example, you can edit the same file in Desktop MATLAB and MATLAB Online, then run it from MATLAB Mobile. MATLAB Online is a MathWorks hosted MATLAB running on the cloud that you can use from any device, without needing to install MATLAB. You can use online sharing to share content, such as live scripts, with others. You can bring your concepts to life using live scripts, which allow you to combine text, equations, images, and code examples in a single document. MATLAB Mobile allows you to use MATLAB from anywhere, including on the go. You can perform computations, run scripts, and view plots from any device, including your phone. You can also collect sensor data from your device, such as acceleration, orientation, and position data, and then import that data into MATLAB. Install MATLAB Mobile today or begin using MATLAB Online to try these features yourself. Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 2242 MATLAB
Managing Design Data in MATLAB and Simulink
 
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You will learn how to manage design data in MATLAB® and Simulink® with a focus on Simulink capabilities, design tradeoffs, and use cases for managing data. Implementing the appropriate data management approach in MATLAB and Simulink facilitates more efficient development workflows and improves development process integrity. Model design data can be managed with the base workspace, model workspace, and data dictionary. This video will contrast their respective capabilities relative to important considerations such as: Data scope: Determining where the data is visible, and where it can be used. Data storage: Storing the data with the model file or in a separate file. Automatic synching: Detecting and syncing changes made in-memory with the source file on disk. Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 245 MATLAB
Simulating Unmanned Aerial Vehicles (UAV) with MATLAB and Simulink
 
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Simulate Unmanned Aerial Vehicles (UAV) in MATLAB and Simulink using the UAV Library for Robotics System Toolbox™. You can simulate fixed-wing or multirotor UAVs using a guidance model that approximates a closed-loop autopilot controller with a kinematic model. Robotics System Toolbox UAV Library: http://bit.ly/2DVizkJ Learn more about Aerospace Blockset: http://bit.ly/2DO1M2W You can also implement a waypoint-following controller and tune its parameters using the low-fidelity model. Furthermore, you can use the same parameters to control the UAV with a high-fidelity model built with Aerospace Blockset™. This workflow is useful to simulate small off-the-shelf drones for various applications such as inspection, monitoring, surveillance, etc. The UAV Library for the Robotics System Toolbox contains the following reference examples: Tuning Waypoint Follower for Fixed-Wing UAV Approximate High-Fidelity UAV model with UAV Guidance Model block Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 760 MATLAB
Programming Raspberry Pi with Simulink
 
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Install the Raspberry Pi® support package and download an image inversion model to Raspberry Pi using a step-by-step workflow with Simulink®. Get a Free Product Trial: https://goo.gl/ScEHEe Raspberry Pi Programming with MATLAB and Simulink: http://goo.gl/7IMV8k Raspberry Pi Support from Simulink: http://goo.gl/4mbMVv
Views: 91409 MATLAB
Understanding Control Systems, Part 5: Simulating Robustness to System Variations in Simulink
 
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This demonstration uses a car to show how you can use Simulink® to simulate robustness to system variations. Download model: http://bit.ly/2QemJKT Watch other MATLAB Tech Talks: https://goo.gl/jD0uOH Get a free Product Trial: https://goo.gl/C2Y9A5 The video models and simulates the car with variations such as different number of passengers. The goal is to maintain the speed of the car at a certain value. The video shows that system variations affect open-loop system behavior and open-loop control needs calibration each time system parameters vary. You will see how feedback control deals with system variations such as different number of passengers.
Views: 12257 MATLAB
Sensor Fusion for Orientation Estimation - MATLAB and Simulink Robotics Arena
 
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Download the files used in this video: http://bit.ly/2E3YVml Sensors are a key component of an autonomous system, helping it understand and interact with its surroundings. In this video, Roberto Valenti joins Connell D'Souza to demonstrate using Sensor Fusion and Tracking Toolbox™ to perform sensor fusion of inertial sensor data for orientation estimation. This is a common and important application for teams participating in maritime and aerial vehicle competitions. First, Connell and Roberto introduce common inertial sensors like inertial measurement units (IMU) and magnetic, angular rate, and gravity (MARG) before explaining why sensor fusion is important to make sense of this sensor data. Roberto will then use MATLAB Mobile™ to stream and log accelerometer, gyroscope, and magnetometer sensor data from his cell phone to MATLAB® and perform sensor fusion on this data to estimate orientation using only a few lines of code. The imufilter and ahrsfilter functions used in this video use Kalman filter-based fusion algorithms. The results of the fusion are compared with the orientation values streamed from the cell phone to check the accuracy of the estimation. Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 336 MATLAB
Defect Detection Inference on Arm Cortex A from MATLAB
 
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In this video, we demonstrate an industrial automation application using deep learning to classify defective parts on an assembly line in MATLAB®. MATLAB provides a complete integrated workflow for engineers and scientists to explore, prototype, and deploy deep learning algorithms in a familiar development environment with built-in higher-level apps and libraries. Download this example: http://bit.ly/2E23Vb8 Resources for Deep Learning with MATLAB: http://bit.ly/2E6jNJW Deep Learning Prediction with ARM Compute: http://bit.ly/2E5KP3T Deep Learning Inference for Object Detection on Raspberry Pi: http://bit.ly/2E5I8zp Using MATLAB Coder™, you can generate C++ code for the complete inference pipeline with image acquisition, pre-processing, and post-processing logic around a trained network, and deploy to any Arm Cortex A based platforms like the Raspberry Pi or HiKey 960 or NXP i.Mx family of processors, to name a few. Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 212 MATLAB
Acquiring Data from Sensors and Instruments Using MATLAB
 
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Free MATLAB Trial: https://goo.gl/yXuXnS Request a Quote: https://goo.gl/wNKDSg Contact Us: https://goo.gl/RjJAkE Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe In this webinar, we will discuss the latest data acquisition capabilities provided by MATLAB and the test and measurement toolboxes. These products enable you to control and acquire data from external sources including sensors such as thermocouples and IEPE accelerometers, sound cards, oscilloscopes, arbitrary waveform generators, and signal analyzers. Through discussion and product demonstrations, you will see how you can use the data acquisition products to: • Acquire data from thermocouples, IEPE accelerometers and sound cards • Generate a PWM signal using counter/timers on DAQ hardware • Connect to a Bluetooth sensor or device • Control and acquire data from oscilloscopes without writing code • Control a stepper motor using Digital I/O
Views: 11749 MATLAB
Drone Simulation and Control, Part 1: Setting Up the Control Problem
 
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Quadcopters and other styles of drones are extremely popular, partly because they have sophisticated programmed control systems that allow them to be stable and fly autonomously with very little human intervention. Their four propellers are spun in precise ways to control the quadcopter in six different degrees of freedom. This is the first video in a series in which we walk through the process of designing a control system that will get a drone to hover at a fixed altitude. • Simulink Hardware Support Package for PARROT Minidrone: http://bit.ly/2C99ynb • Introduction to Simulink Hardware Support for PARROT Minidrones: http://bit.ly/2CapENk This video describes the sensors that come with the Mambo, a parrot minidrone that interfaces with MATLAB® and Simulink®. Future videos will show how we can use these sensors to estimate system states like altitude and speed. • Quadcopter Simulation and Control Made Easy: http://bit.ly/2CcnHjl • Modelling, Simulation, and Control of a Quadcopter: http://bit.ly/2CeFI0H This video also describes how the four propellers can be configured and spun in specific ways that allow the drone to independently roll, pitch, yaw, and thrust. • Quadcopter Modelling and Simulation: A Case Study for Encouraging Deeper Learning Engagements with Students: http://bit.ly/2Cf08GS • How to Design and Model a Quadcopter Prototype with Simulink and Arduino: http://bit.ly/2CcnKvz With knowledge of the sensors, actuators, and the dynamics of the drone itself, we’ll be prepared to develop the control system over the rest of this series. • Programming Drones with Simulink: http://bit.ly/2CdbFq7 • Quadcopter Modelling with Simulink: http://bit.ly/2CbdeVj Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbL See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 8404 MATLAB
Simulink Product
 
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Views: 70 TeraSoftTW
Modeling, Simulation, and Flight Control Design of an Aircraft with Simulink
 
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See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Download a trial: https://goo.gl/PSa78r In this webinar, you will learn how you can apply Model-Based Design with MATLAB and Simulink for air vehicle design and automatic flight control. Engineers working in the aerospace field can use MATLAB and Simulink to improve the design workflow for: • Defining aircraft geometry and importing DATCOM data to define vehicle forces and moments • Creating a simulation to understand the vehicle dynamics • Designing a flight control system with automatic gain generation to stabilize the vehicle and meet requirements • Performing simulations to verify the design and visualize the simulation in a realistic 3D environment The primary focus is for engineers whose workflow involves modeling, simulation, and control of aircraft. Many of the Model-Based Design and control concepts shown in this webinar can be applied to a variety of applications. About the Presenter: Ryan Gordon is the product manager for Aerospace Toolbox and Aerospace Blockset at MathWorks. Before joining MathWorks, Ryan developed models and control algorithms for autonomous UAVs using Simulink at Northrop Grumman Aerospace Systems. He has a bachelor’s degree in aerospace engineering from Saint Louis University and a master’s degree in aerospace engineering focusing on dynamics and control from USC.
Views: 27287 MATLAB
SysML model integration with MATLAB Simulink®
 
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Learn more at: http://www.nomagic.com/mbse/ Webinar Description: Systems Modeling Language (SysML) is used to capture systems design as descriptive and analytical system models, which relate text requirements to the design and provide a baseline to support analysis and verification. SysML Parametric models enforce requirements and capture constraints on the performance or physical properties of systems, which can then be evaluated by an appropriate analysis tool to support wide variety of engineering analysis and simulations. This session demonstrates: -SysML Parametric modeling concepts -Practical use cases of Parametric model analysis -Cameo Simulation Toolkit as parametric evaluation tool -Different constraint specification languages and solvers -How to set and use a MATLAB as a parametric solver -Integrating MATLAB functions into parametric models -Simulink integration alternatives The session was hosted by Nerijus Jankevicius, Product Manager at No Magic Inc., Learn more at: http://www.nomagic.com/mbse/
Views: 6095 No Magic
Computer Vision with MATLAB for Object Detection and Tracking
 
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Download a trial: https://goo.gl/PSa78r See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Computer vision uses images and video to detect, classify, and track objects or events in order to understand a real-world scene. In this webinar, we dive deeper into the topic of object detection and tracking. Through product demonstrations, you will see how to: Recognize objects using SURF features Detect faces and upright people with algorithms such as Viola-Jones Track single objects with the Kanade-Lucas-Tomasi (KLT) point tracking algorithm Perform Kalman Filtering to predict the location of a moving object Implement a motion-based multiple object tracking system This webinar assumes some experience with MATLAB and Image Processing Toolbox. We will focus on the Computer Vision System Toolbox. About the Presenter: Bruce Tannenbaum works on image processing and computer vision applications in technical marketing at MathWorks. Earlier in his career, he developed computer vision and wavelet-based image compression algorithms at Sarnoff Corporation (SRI). He holds an MSEE degree from University of Michigan and a BSEE degree from Penn State. View example code from this webinar here: http://www.mathworks.com/matlabcentral/fileexchange/40079
Views: 65979 MATLAB
Autopilot Development Using Model-Based Design - MATLAB and Simulink Robotics Arena
 
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To design custom autopilots, Claudio Conti of Sapienza Flight Team at Sapienza University of Rome joins Connell D’Souza of MathWorks to talk about using Model-Based Design to develop a custom autopilot. The Sapienza Flight Team competes in AUVSI’s Student Unmanned Aerial Vehicle Competition (SUAS) where teams design autonomous fixed- or rotary-wing aircrafts to perform search and reconnaissance tasks. Create and Configure MATLAB S-Functions: http://bit.ly/2E5oZgY Learn more about Simulink: https://goo.gl/nqnbLe Code Generation Tutorials: http://bit.ly/2E7cCRQ Claudio will explain the architecture and control strategy in his custom autopilot before demonstrating how they used Simulink® to develop the autopilot model. His team created S-functions to interface with the different sensors on their aircraft, as well as to encode and decode MAVLink messages to communicate with the autopilot from the ground station before using the code generation capabilities in Simulink to deploy it to a custom-built avionics box consisting of an Arduino® Due and a Raspberry Pi™ connected over serial. To simulate this autopilot, Claudio and his team employed real-time hardware-in-the-loop simulation techniques using a dSPACE® real-time machine. They used Aerospace Blockset™ to model their airplane, the environment, and sensors in Simulink before deploying it to a dSPACE real-time machine using the Real-Time Interface. This simulation involves an aircraft, environment, and sensor models running on the dSPACE machine, the autopilot running on the avionics box, QGroundControl and FlightGear Flight Simulator to visualize the trajectory on a desktop computer, and MAVLink is used to communicate between the different nodes. Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 502 MATLAB
Physical Modeling with Simscape
 
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Simscape™ makes it easy to model physical systems, including electrical, mechanical, and hydraulic components. Learn more about Simscape: http://goo.gl/Jhsth7 Get a free Product Trial: https://goo.gl/5NvCdU A hydraulic scissor jack, controlled by an electrical circuit, is used to show some of the modeling, simulation, and deployment capabilities of Simscape. With Simscape you can: • Model electrical, mechanical, and hydraulic systems • Create custom components with Simscape language • Enhance your models with Simscape add-on libraries • Detect causes of slow simulations profiling tools • Run simulations in real-time for HIL testing Physical systems often span multiple physical domains, and require modeling techniques beyond input/output blocks and transfer functions. Simscape enables engineers to use physical modeling methods within the Simulink environment, and leverages solver technology designed for physical systems. With the foundation library, Simscape language, and add-on libraries, a wide range of systems can be modeled, including multibody systems. Teams working with Simscape models can share without needing a license for add-on products while performing tasks such as parameter sweeps and code generation for HIL testing.
Views: 33084 MATLAB
Introduction to Model Based Design Modeling and Simulation with Simulink
 
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Free MATLAB Trial: https://goo.gl/yXuXnS Request a Quote: https://goo.gl/wNKDSg Contact Us: https://goo.gl/RjJAkE Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe ------------------------------------------------------------------------- Explore Simulink, an environment for multidomain simulation and Model-Based Design for dynamic and embedded systems. Through product demonstrations, you will see a high-level overview of the major capabilities and how you can use Simulink to design, simulate, implement, and test a variety of time-varying systems, including communications, controls, signal processing, video processing, and image processing. This webinar is for people who may be unfamiliar with Simulink.
Views: 53275 MATLAB
Understanding Control Systems, Part 4: Simulating Disturbance Rejection in Simulink
 
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This demonstration uses a car to show how you can simulate open- and closed-loop systems in Simulink®. Download model: http://bit.ly/2Qau7XO Watch other MATLAB Tech Talks: https://goo.gl/jD0uOH Get a free Product Trial: https://goo.gl/C2Y9A5 First, you will learn how to model and tune open-loop systems. The goal of the demonstration is to maintain the speed of a car. Then, you’ll explore the behavior of the open-loop system in the presence of a disturbance. To illustrate disturbance rejection, the video shows how to model and simulate a feedback control system . You will gain insight into how feedback control compensates for disturbance. You’ll investigate signals such as error (in this example, the error is the difference between the measured and desired output), actuating signal (here, the actuating signal is the pedal’s position) and system output (in this example, the output is speed).
Views: 25193 MATLAB
ATRIAS Robot: Walking with MATLAB Simulation and Control
 
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Robot control was implemented using Simulink Real-Time, a Mathworks product. Same MATLAB code was used to control both the simulator and ATRIAS. Collaborating ATRIAS Investigators: Dr. Jonathan Hurst, Oregon State University Dr. Hartmut Geyer, Carnegie Mellon University Dr. Jessy Grizzle, University of Michigan ATRIAS is on Twitter: @ATRIASrobot twitter.com/ATRIASrobot Dynamic Robotics Laboratory: http://mime.oregonstate.edu/research/drl/ School of Mechanical, Industrial and Manufacturing Engineering Oregon State University Principal Investigator: Dr. Jonathan Hurst Funded by the Defense Advanced Research Projects Agency (DARPA), the Human Frontier Science Program (HFSP), and the National Science Foundation (NSF)
Simulink Workshop 05: Simulink Integration with MATLAB
 
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This video was created by Dr. Nolan Tsuchiya with support from a Cal Poly Pomona SPICE grant for Instructional Innovation. Written and produced by Dr. Nolan Tsuchiya in the Mechanical Engineering Department at Cal Poly Pomona. Want to see more Mechanical Engineering tutorial videos? Visit http://www.cpp.edu/~meonline for hundreds of free videos produced by our very own faculty members.
Views: 254 CPPMechEngTutorials
Matlab Tutorial - 25 - Calculating the Vector Dot Product and Cross Product
 
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Get more lessons like this at http://www.MathTutorDVD.com Learn how to calculate the dot product between two vectors using matlab. We will also learn how to enter and calculate the vector cross product using matlab.
Views: 205 mathtutordvd
Best Practices for Verification and Validation
 
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See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Download a trial: https://goo.gl/PSa78r In this webinar you will learn techniques and practices in Model-Based Design to verify and validate software designs and embedded code using MathWorks tools. We will address requirements driven development, model coverage testing, and static code analysis of embedded software. About the Presenters: Nishaat Vasi is a product marketing manager at MathWorks. Since joining MathWorks in 2007, Nishaat has partnered with customers involved in high-integrity applications to promote the adoption of MathWorks tools for embedded software verification. He holds an M.S. in electrical engineering from University of Massachusetts and a B.E. in electronics engineering from University of Mumbai. Jay Abraham is a product marketing manager at MathWorks. His area of expertise is in software tools for the verification of critical embedded applications. He has over 20 years of software and hardware design experience. Jay has an M.S. in computer engineering from Syracuse University and a B.S. in electrical engineering from Boston University
Views: 4340 MATLAB
Release 2016a Highlights
 
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Download the free white paper “MATLAB and Simulink Version Upgrades for Large Organizations: https://goo.gl/VYD7BB Release 2016a includes new releases of MATLAB® and Simulink® as well as updates and bug fixes to all other products. R2016a includes updates to MATLAB, Simulink, and 83 other products. There are many new features in the MATLAB product family. The Live Editor provides a new way to create and run MATLAB programs. Your live scripts can be shared as interactive documents. App Designer allows you to design and program interactive applications. You can visualize data and equations with new plotting functions, and pause your running MATLAB programs. New toolbox functionality lets you use deep learning for image classification, and optimize trading performance through transaction cost analysis. Updates to apps include: The training of multiple classification models, improved interactive tuning of SISO controllers, model-order reduction, and optical character recognition training. There is also new hardware support for Kinect version 2 and USB3 Vision. Enhancements to the Simulink product family include: A new start page and automatic solver to set up and simulate your models faster, simulation of systems that target heterogeneous embedded devices, the ability to visualize and check units more easily, and new blocks for defining and propagating variants and visualizing flight conditions. Use the new discrete-event simulation engine and block library to dynamically schedule your embedded software. You can also perform sensitivity analysis of Simulink models, and use three-way merge to resolve conflicts between model revisions. In addition, the Simscape product family has advanced its equation reduction and simulation technology, enabled run-time parameter tuning in generated code, and added a new thermal liquid library. There are feature enhancements and two new products that can help you with wireless and audio system design. You can also take advantage of code generation and verification improvements including new medical standards support in the IEC Certification Kit. Explore the new capabilities in R2016a, and download the latest versions of your products now.
Views: 29911 MATLAB
Modeling Pneumatic Robot Actuators, Part 1 - MATLAB and Simulink Robotics Arena
 
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Download the files used in this video: http://bit.ly/2QE71ci Join Veer Alakshendra and Maitreyee Mordekar as they discuss modeling and simulation of a pneumatic system relevant to robotics competitions such as Robocon. Veer and Maitreyee show how you can model a pneumatic system by using physical blocks available in SimscapeTM. You can use blocks like a constant volume chamber, a directional control valve, and translational mechanical converters to build a pneumatic circuit. Veer and Maitreyee also show how heat transfer effects due to convection have been taken into consideration. Finally, they discuss the results where they compare the load position and pressure in the chamber for different values of loads. Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 728 MATLAB
Sampling Signals Part 2 (2/10) - Time-Bandwidth Product Matlab Script
 
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http://adampanagos.org The previous video in this playlist introduced the time-duration (Td) and bandwidth metrics (Bw) and said that the time-bandwidth product was always greater than or equal to 1/2 (i.e Td*Bw greater than or equal to 0.5). This video examines the this time-bandwidth product via experimenting with a Matlab script. The continuous time-signal x(t) = exp(-a|t|) is parameterized by "a" which controls how quickly the signal decays in the time-domain. The spectrum of this signal (i.e. X(w)) is computed analytically and both Td and Bw are computed numerically in Matlab. We show that as "a" varies, the spectrum changes accordingly (compression in time leads to expansion in frequency and expansion in time leads to compression in frequency). Also, regardless of the values of Td and Bw, we still have Td*Bw greater than or equal to 1/2 as expected. If you enjoyed my videos please "Like", "Subscribe", and visit http://adampanagos.org to setup your member account to get access to downloadable slides, Matlab code, an exam archive with solutions, and exclusive members-only videos. Thanks for watching!
Views: 2269 Adam Panagos
Programming Robot Swarms - MATLAB and Simulink Robotics Arena
 
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Sebastian Castro introduces a general workflow for designing robot swarm behavior using MATLAB® and Simulink®. This includes prototyping the robot behavior, testing it with a simple simulation, and then using automatic code generation to target external software and hardware. Visit the MATLAB and Simulink Robotics Arena: http://bit.ly/2DUdmJW Get started with Mobile Robotics Simulation Toolbox: http://bit.ly/2DUgi9d Download the Mobile Robotics Simulation Toolbox: http://bit.ly/2DVfmSd The first software example demonstrates textual programming with MATLAB and generation of standalone code with MATLAB Coder™. The second example shows graphical programming with Simulink and Stateflow®, and deployment to an external simulator with Embedded Coder®. Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 619 MATLAB
Understanding Model Predictive Control, Part 7: Adaptive MPC Design with Simulink and MPC Toolbox
 
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In this video, you will learn how to design an adaptive MPC controller for an autonomous steering vehicle system whose dynamics change with respect to the longitudinal velocity. After you design an MPC controller for the most likely operating conditions of your control system, you can implement an adaptive MPC controller based on that design. At each time step, adaptive MPC updates the plant model and nominal conditions for the current operating conditions. In this video, you’ll learn how to calculate and update the discrete plant model required by the adaptive MPC block. You’ll also learn how to generate code from your adaptive MPC controller, and you’ll see an example showing a real self-driving car that uses MPC control and image processing algorithms to keep itself within its lanes. • Free Technical paper on Adaptive Cruise Controller with Model Predictive Control: http://bit.ly/2JhmOYr • Download the model used in this video: http://bit.ly/2JiKKdW • What is Model Predictive Control Toolbox: http://bit.ly/2xfEe2M • Lane Keeping Assist System Example: http://bit.ly/2xh6lhR • Lane Keeping Assist System Using Model Predicitve Control Example: http://bit.ly/2xhlRKK • Lane Keeping Assist with Lane Detection: http://bit.ly/2xgSatq • Obstacle Avoidance Using Adaptive Model Predictive Control: http://bit.ly/2Jgzw9B • Developing Longitudinal Controls for Self-Driving Taxi: http://bit.ly/2xjL9rF Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 2638 MATLAB
Release 2015b Highlights
 
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Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Release 2015b includes updates to MATLAB®, Simulink®, and 83 other products. In the MATLAB product family: The new execution engine runs MATLAB code faster The Add-On Explorer allows you to find and install add-ons from MathWorks and the user community New functions let you create and visualize graphs and networks The redesigned navigation and keyboard shortcuts in documentation help you find information faster You can deploy MATLAB components and integrate them with Python applications, and perform 3D point cloud processing such as geometric shape fitting. New machine learning capabilities include PCA feature transformation in the Classification Learner app, and GPU acceleration for 65 functions. In the Simulink product family, you’ll find: A new interface in the Scope block Multilingual block and signal names Always-on tuning of block parameters and workspace variables The ability to create reusable project components. You can queue new message objects that carry data in Stateflow®, do faster parameter estimation and response optimization using both parallel computing and Simulink Fast Restart, and perform vulnerability detection checks on your code. For signal processing, you’ll see enhanced waveform generation, and new analysis and visualization capabilities. Also take advantage of code generation enhancements and new hardware support. Explore the new capabilities in Release 2015b, and download the latest versions of your products.
Views: 28110 MATLAB
How to Specify the Execution Domain in Simulink
 
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Specify the execution domain of your subsystem or model blocks or have Simulink deduce it to improve your solver performance and code. Simulink will deduce whether your component is discrete, dataflow, or not discrete. By specifying whether your component is discrete or dataflow, you can lock down the boundary of that component and Simulink will make sure you don't accidentally introduce blocks that are incompatible. Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 522 MATLAB
Deep Learning with NVIDIA Jetson and ROS - MATLAB and Simulink Robotics Arena
 
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Download the files used in this video: http://bit.ly/2QAL9OL Jon Zeosky and Sebastian Castro discuss how algorithms designed in MATLAB® can be deployed as standalone CUDA® code to target NVIDIA® GPUs, and how this standalone code can be used in a development process involving Robot Operating System (ROS). In the software demonstration, Jon and Sebastian first use a pretrained neural network in MATLAB to create a deep learning classification algorithm. Then, they use GPU Coder™ to generate a standalone library from this algorithm and deploy it to an NVIDIA Jetson™ platform. Finally, they integrate the generated library into a ROS node developed in C++ to connect with other software nodes running on the network. Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 439 MATLAB
Understanding Model Predictive Control, Part 6: How to Design an MPC Controller with Simulink
 
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Learn how to design an MPC controller for an autonomous vehicle steering system using Model Predictive Control Toolbox™. •Download the Simulink model used in this video:http://bit.ly/2QcllZj •Learn more about Model Predictive Control Toolbox: http://bit.ly/2xgwWvN •Model Predictive Control Toolbox: http://bit.ly/2xfEe2M •Lane Keeping Assist System Documentation: http://bit.ly/2xh6lhR •Lane Keeping Assist System Using Model Predictive Control: http://bit.ly/2xhlRKK •Lane Keeping Assist with Lane Detection: http://bit.ly/2xgSatq •Implementing an Adaptive Cruise Controller with Simulink: http://bit.ly/2xent84 •Developing Longitudinal Controls for a Self-Driving Taxi: http://bit.ly/2xjL9rF •Driving Scenario Designer: http://bit.ly/2xpx0JN •Autonomous Vehicle Steering Using Model Predictive Control: http://bit.ly/2xiXsV3 This video walks you through the design process of an MPC controller. Using the MPC Designer app that comes with Model Predictive Control Toolbox, you can specify MPC design parameters such as controller sample time, prediction and control horizons, and constraints and weights. You can then fine tune your controller and evaluate its performance. For the autonomous steering vehicle example demonstrated in this video, a custom reference trajectory is created using the Driving Scenario Designer app, which is part of Automated Driving System Toolbox™. Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 9867 MATLAB
MATLAB webinar - Identification and control design with Simulink
 
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Join this 30-minute prerecorded webinar with live Q&A session and learn about useful features of Simulink product. Topics included: Control Design using Response Optimization Including Uncertain Variables System Identification using the System Identification App and the PID Tuner App Control Design using the PID Tuner App Control Design for Nonlinear Systems using Gain Scheduling Type in your question into the chat window and our application engineer will answer it after the recording ends.
Views: 71 Gamax Ltd.
Drone Simulation and Control, Part 3: How to Build the Flight Code
 
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This video describes how to create quadcopter flight software from the control architecture developed in the last video. It covers how to process the raw sensor readings and use them with the controllers to calculate motor speed commands. Check out "Drone Control and the Complementary Filter" on Brian's Channel: https://youtu.be/whSw42XddsU • Simulink Hardware Support Package for PARROT Minidrone: http://bit.ly/2C99ynb • Introduction to Simulink Hardware Support for PARROT Minidrones: http://bit.ly/2CapENk • Quadcopter Simulation and Control Made Easy: http://bit.ly/2CcnHjl • Modelling, Simulation, and Control of a Quadcopter: http://bit.ly/2CeFI0H In addition to those two functions, the flight control software is also responsible for reference command generation, data logging, and fault protection. You’ll see how each of these contribute to getting a quadcopter to hover safely. • Quadcopter Modelling and Simulation: A Case Study for Encouraging Deeper Learning Engagements with Students: http://bit.ly/2Cf08GS • How to Design and Model a Quadcopter Prototype with Simulink and Arduino: http://bit.ly/2CcnKvz • Programming Drones with Simulink: http://bit.ly/2CdbFq7 Quadcopter Modelling with Simulink: http://bit.ly/2CbdeVj The quadcopter example in Simulink® is used as a starting point for the flight software and you’ll learn how to load and run the code on the Parrot® Minidrone directly from Simulink. Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 4117 MATLAB
Control Systems in Practice, Part 2: What is Gain Scheduling?
 
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Often, the best control system is the simplest. When the system you’re trying to control is highly nonlinear, this can lead to very complex controllers. This video continues our discussion on control systems in practice by talking about a simple form of nonlinear control: gain scheduling. •Implement Gain-Scheduled PID Controllers Example: http://bit.ly/2NE9Ybi •Gain-Scheduled Control of a Chemical Reactor Example: http://bit.ly/2NFT8ZD •Model Gain-Scheduled Control Systems in Simulink: http://bit.ly/2NDfcEh •Tuning of Gain-Scheduled Three-Loop Autopilot: http://bit.ly/2NERreU Gain scheduling is a method that adjusts the gains of a linear controller based on the current state of the system. In this way, a gain scheduled controller can produce adequate control over the entire operating range of the system by splitting the range into smaller, linearly controllable areas. Switching between controller gains, however, can have some unintended effects. In addition to providing an intuitive understanding of gain scheduling, this video walks through some ways to implement these controllers and how the chosen implementation can minimize some of the negative effects of switching gains. Gain scheduling is used often in practice, so it is worthwhile to learn the basics of this method with this video. However, nothing is better than sitting down and practicing it on your own. Here are some other tutorials and examples that will help you get started designing a gain scheduled controller in MATLAB® and Simulink®. Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe Learn more about Powertrain Blockset: https://goo.gl/ssotUh See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 2814 MATLAB
Understanding Kalman Filters, Part 6: How to Use a Kalman Filter in Simulink
 
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This video demonstrates how you can estimate the angular position of a simple pendulum system using a Kalman filter in Simulink. Design and use Kalman filters in MATLAB and Simulink: https://goo.gl/SVA9IK Download model: http://bit.ly/2QcmPml Watch other MATLAB Tech Talks: https://goo.gl/jD0uOH Get a free Product Trial: https://goo.gl/C2Y9A5 Using MATLAB and Simulink, you can implement linear time-invariant or time-varying Kalman filters. In this video, a simple pendulum system is modeled in Simulink using Simscape Multibody™. The angular position of the pendulum is estimated using the Kalman filter block that is available in Control System Toolbox™. The video shows how to configure Kalman filter block parameters such as the system model, initial state estimates, and noise characteristics.
Views: 29371 MATLAB
Modeling a DC Motor
 
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Simscape™ is used to model a DC motor. Learn more about Simscape: http://goo.gl/Jhsth7 Get a free Product Trial: https://goo.gl/5NvCdU The model is created by assembling a physical network of Simscape components, including electrical resistors, shaft inertias, and friction. The simulation results are evaluated in the Simscape Results Explorer. The physical connections used in the model make it easy to understand, modify, and maintain, and make it possible to quickly build up models spanning multiple physical domains.
Views: 11744 MATLAB
How to Run MATLAB in the Cloud with Amazon Web Services
 
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Learn how to get MATLAB® up and running in Amazon Web Services (AWS) with the help of a reference architecture. The reference architecture incorporates best practices for creating a full MATLAB desktop experience, including a connection from your local desktop using Remote Desktop Protocol (RDP). - MATLAB in Amazon Web Services: http://bit.ly/2DQtmwe - Go to the MATLAB on AWS reference architecture in GitHub: http://bit.ly/2DQkHtX When running MATLAB in AWS, you can efficiently access data you store in Amazon S3 and analyze that data quickly by taking advantage of on-demand, high-performance compute resources available in AWS. All you need is a MATLAB license configured for cloud use and an AWS account with appropriate permissions. Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 846 MATLAB
Matlab Video Tutorial:  Multiplying Matrices and Vectors
 
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http://www.FreedomUniversity.TV. A series of tutorial videos and examples on using matlab to solve problems. For questions, please contact Professor Santiago at [email protected] or visit the above website.
Views: 68189 John Santiago
Complete MATLAB Tutorial for Beginners
 
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Get The Complete MATLAB Course Bundle! https://josephdelgadillo.com/product/matlab-course-bundle/ Limited FREE coupons! https://goo.gl/xejcB1 Get the courses directly on Udemy! Go From Beginner to Pro with MATLAB! http://bit.ly/2v1e0lL Machine Learn Fundamentals with MATLAB! http://bit.ly/2v3sQs6 The Ultimate Guide for MATLAB App Development! http://bit.ly/2GOodDN MATLAB for Programming and Data Analysis! http://bit.ly/2IIwpWL MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-generation programming language which is frequently being used by engineering and science students. In this course, we will start learning MATLAB from a beginner level, and will gradually move into more technical and advanced topics. This course is designed to be general in scope which means that it will be beneficial to students in any major. Once, passed a certain learning thresholds, you will definitely enjoy MATLAB Programming. The key benefit of MATLAB is that it makes the programming available to everyone and is very fast to turn ideas into working products compared to some of the conventional programming languages such as Java, C, C++, visual basic and others. Topics covered in the course: Instructor and Course Introduction Handling variables and Creating Scripts Doing Basic Math in MATLAB Operations on Matrices Advance Math Functions with Symbolic Data Type Interacting with MATLAB and Graphics Importing Data into MATLAB File Handling and Text Processing MATLAB Programming Sharing Your MATLAB Results Cell Data Type Tables and Time Tables Working with Structures and Map Container Data Type Converting between Different Data Types
Views: 186351 Joseph Delgadillo
Modeling Pneumatic Robot Actuators, Part 3 - MATLAB and Simulink Robotics Arena
 
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Download the files used in this video: http://bit.ly/2QE71ci Join Veer Alakshendra and Maitreyee Mordekar as they discuss the control strategy for targeting a ball at the target relevant to robotics competitions such as Robocon. Veer and Maitreyee first show how you can extend Simscape Multibody throwing mechanism models with physical effects modeled in SimscapeTM. Then they build a closed-loop control system to produce the required amount of spool input for the desired piston movement. PID controller block has been used to control the system. Finally, Veer and Maitreyee show that with proper tuning of the PID tuner, the ball can successfully land in the yellow box. Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 376 MATLAB
80 CATIA Assembly Tutorial: Inserting components in Assembly(product)
 
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►For more CATIA Tutorials:►https://www.youtube.com/playlist?list=PLkMYhICFMsGbYCvbGrrygtqGiBGguIzbf ► For more Inventor Tutorials :►https://www.youtube.com/playlist?list=PLkMYhICFMsGaqeVXGsgJy__TGWHnotJol ► Inventor Beginner Series :►https://www.youtube.com/playlist?list=PLkMYhICFMsGYkVrkVbX4xngskLzxTBStJ ► MATLAB Tutorials: ► http://www.youtube.com/playlist?list=PLkMYhICFMsGZfEMQda72NL9kHUb__LsmK ►SolidWorks Tutorial Channel: ► https://www.youtube.com/channel/UCtwaWPOXEBysZLh1rrPzwFw ►MATLAB Beginners Tutorials : ►http://www.youtube.com/playlist?list=PLkMYhICFMsGZfEMQda72NL9kHUb__LsmK ------------------------------------------------------------------------- Rating, commenting, subscribing and sharing are always appreciated!! -------------------------------------------------------------------------- Hello & Welcome to TrainingEngineer Channel This channel is dealing with the most used softwares by Engineers & technical students or anyone interested! This channel will provide the tutorials and trainings which related to those software! Also it will be helpful to whom intended to learn at home! This Channel will contain topics about: ► MATLAB TUTORIALS -Matlab for beginners, Matlab mathematics - MATLAB simulink ► Inventor TUTORIALS: Autodesk inventor for beginners - Inventor sketch - Inventor features - Autodesk Inventor practical examples ► CATIA V6 TUTORIALS Future Plans: ► Hydroponics Engineering ► Arduino & Electronics Projects ►►►SUBSCRIBE ◄◄◄ KeyWords: AUTODESK INVENTOR - CATIA - 3D Modelling - Engineering Drawing & Programming - Matlab tutorials - Matlab programming OTHERS: ►SolidWorks Tutorials: ► http://www.youtube.com/playlist?list=PLfP1GxQ1lPaQ_F5UUAuF3pxbP4q6Jqpy2 ►SolidWorks Surface Tutorials: ► https://www.youtube.com/playlist?list=PLfP1GxQ1lPaSwJCvC0KFe7s472ZcCY4df ►Solidworks Sketch Tutorials: ► https://www.youtube.com/playlist?list=PLfP1GxQ1lPaTpiP_AvdjXdBtCX3CKqgkt ►SolidWorks Beginner Tutorial: ► https://www.youtube.com/playlist?list=PLfP1GxQ1lPaSwJCvC0KFe7s472ZcCY4df Donate :►https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=W4F4JB7AYYUNJ
Views: 46906 Tutorials Engineer ҂
Experiences of Introducing Model Based Design at Danfoss Solar Inverters
 
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Learn more about simulating digital control for power electronics: http://bit.ly/2P0GMff When Danfoss Solar Inverters prepared to start a new product development project, they realized that they needed to establish a process to support embedded software development. Due to the benefits of linking the specification, design, test, and target implementation phases closer together, Model-Based Design was chosen. Learn more about power electronics simulation: http://bit.ly/2PCHWdR Download a trial: http://bit.ly/2P77UJK Because many of the members of the start up design team were new, Model-Based Design was introduced through an evolutionary change rather than a revolutionary change to an existing design process. This presentation outlines how the team adopted the new tools and created a new PV inverter that met their schedule and quality goals. The model design has established a solid foundation for Danfoss Solar Inverters to build on in future product development projects." © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 467 MATLAB
Introduction to Machine Learning, Part 1: Machine Learning Fundamentals
 
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Try Machine Learning now: - Train a Machine Learning model right in your browser: http://bit.ly/2QCnU6X - Get a Free Machine Learning Trial: http://bit.ly/2QKxhBX Explore the fundamentals behind machine learning. Learn about two common machine leaning approaches: Unsupervised learning, which finds hidden patterns in input data Supervised learning, which trains a model on known input and output data so that it can predict future outputs. - MATLAB for Machine Learning: http://bit.ly/2QIDlum - Machine Learning with MATLAB eBook: http://bit.ly/2QBFiZO You’ll also learn about three common techniques within these approaches: Clustering techniques put data into different groups based on shared characteristics in the data. Classification techniques predict discrete responses—like whether an email is genuine or spam. Regression techniques predict continuous responses, such as what temperature a thermostat should be set at or fluctuations in electricity demand. Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 654 MATLAB
Performing Power System Studies
 
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Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 For more videos on Performing Power System Studies visit, https://www.mathworks.com/videos/series/performing-power-system-studies-101128.html Electrical power systems that include advanced measurement infrastructure, large penetrations of distributed energy resources, integration of power converters, and operation of non-standard components, introduce an increased level of both risk and opportunity, compared to traditional power systems. Simulation models are an essential part of power system studies, but a broader computational framework is needed to extract the most value from those studies, and reduce the risk of the decisions that you make from the outcome of those studies. In this webinar, MathWorks and IREQ will demonstrate the following tasks within power system studies through worked examples, Developing simulation models suitable for different time periods Replaying recorded data through a simulation model Developing a customized simulation component Running multiple scenarios on multiple cores Working with large amounts of simulation data Automating the generation of reports Product Focus SimPowerSystems Parallel Computing Toolbox Statistics and Machine Learning Toolbox
Views: 77356 MATLAB
Product video: Data fusion and function development with BASELABS Create and MATLAB
 
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Many developers use Matlab for the development of functions for advanced driver assistance systems and automated vehicles. This video shows how BASELABS Create works seamlessly together with Matlab. For this example, a data fusion application is implemented with BASELABS Create and a brake assist function is added using Matlab.
Views: 955 BASELABS GmbH
MATLAB Apps with ROS - MATLAB and Simulink Robotics Arena
 
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Sebastian Castro and Cameron Stabile discuss how MATLAB® and Robotics System Toolbox™ can help you develop interactive apps that communicate with robots and simulators running the Robot Operating System (ROS). First, Sebastian and Cameron manually control a ROS enabled robot, set parameters, and visualize sensor data in a MATLAB app built with App Designer. Then, they integrate a ROS enabled Simulink® model that performs autonomous waypoint navigation and obstacle avoidance. This model can be run in MATLAB along with the app, or deployed as a standalone ROS node using automatic C/C++ code generation. For more information, check out the following resources: Download the app from the MATLAB Central File Exchange: http://bit.ly/2HTB9HY Learn more about App Designer: http://bit.ly/2LViyxE Learn more about MATLAB, Simulink, and ROS: http://bit.ly/2HW91nE Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 1895 MATLAB
Understanding PID Control, Part 1: What is PID Control?
 
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Explore the fundamentals behind PID control. Chances are you’ve interacted with something that uses a form of this control law, even if you weren’t aware of it. That’s why it is worth learning a bit more about what this control law is, and how it helps. PID is just one form of feedback controller. It is the simplest type of controller that still 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 across a wide range of physical applications. However, often when learning something new in control theory, it’s easy to get bogged down in the detailed mathematics of the problem. So, this video skips most of the math and instead focuses on building a solid foundation. PID Control with MATLAB and Simulink: http://bit.ly/2Qg57y8 PID Control Made Easy: http://bit.ly/2Q7Hhor Watch more MATLAB Tech Talks: http://bit.ly/2rTc8Yp Check out more control system lectures on Brian's Channel: http://bit.ly/2IUlvkw Get a free MATLAB Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 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 maybe trademarks or registered trademarks of their respective holders.
Views: 77077 MATLAB

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