Student Competition Tutorials and Videos

Learn how to use MATLAB and Simulink to solve competition tasks.

Student Competition: Code Generation Training, Overview

Code Generation

Learn how to generate readable, standalone C/C++ code from MATLAB functions and Simulink models. Navigate and customize the generated code before deploying directly onto target hardware boards. Use Simulink as an integration environment and generate code for multirate systems.

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These training materials will help your team get started with designing and simulating mobile robotics algorithms using MATLAB and Simulink.

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Student Competition: Mobile Robotics Training: Overview

Mobile Robotics

Learn how to design and simulate common mobile robotics algorithms in MATLAB and Simulink, such as open- and closed-loop feedback control systems, for your robot to perform tasks like dead reckoning, line following, and obstacle detection. Use custom simulation tools to test algorithms within Simulink before deploying them to an actual robot.

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These training materials will help your student competition team get started with modeling, simulating, and analyzing automotive systems. You’ll learn about longitudinal vehicle dynamics and 3D suspension modelling.

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Student Competition: Physical Modeling Training Overview

Physical Modeling

Get started with modeling, simulating, and analyzing automotive systems, including longitudinal vehicle dynamics and 3D suspension modeling. These tutorials will help your team set up a vehicle model, and predict lap times, fuel consumption, and battery life.

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Hybrid Electric Vehicles

Learn to develop hybrid electric vehicle (HEV) systems using MATLAB and Simulink. Explore motor control design, and how to use equivalent circuits for representing the dynamic behavior of battery cells. Explore battery pack electro-thermal modeling and battery thermal management system design. Learn about modeling and simulating HEV systems, creating plant models, developing control systems, and optimizing your models.

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These training materials will help your team get started with designing and deploying computer vision algorithms using MATLAB and Simulink.

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Student Competition: Computer Vision Training Overview

Computer Vision

Understand fundamental computer vision techniques, such as feature extraction, object detection, text recognition, and point cloud processing. These tutorials will enable your team to design and deploy computer vision algorithms in MATLAB and Simulink to perform tasks such as autonomous recognition of targets and obstacle avoidance.

Detailed Syllabus

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Learn the basics with introductory courses.

MATLAB Onramp

Simulink Onramp

Deep Learning Onramp

MATLAB and Simulink

Primary and Secondary School Competitions Hub

Learn how to use MATLAB and Simulink to design algorithms, create simulations, and speed up development for primary and secondary school competitions.

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MATLAB and Simulink

Racing Lounge

Discover basics of MATLAB and Simulink and in-depth information on automotive development in this continuously evolving video series.

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MATLAB and Simulink

Robotics Arena

Learn how to use MATLAB and Simulink to design algorithms, create simulations, and speed up development for student competitions in robotics and unmanned systems.

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Racing Lounge

Blog

Learn more about best practices and teamwork for student competitions.

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Code Generation with MATLAB	Generate editable, customizable code from MATLAB code using MATLAB Coder.
Preparing and Customizing Generated Code for MATLAB	Prepare and optimize MATLAB code for code generation.
Code Generation with Simulink	Generate editable, customizable code from Simulink models using Simulink Coder.
Customizing Generated Code with Simulink	Customize the code generated from Simulink models to balance various design considerations.
System Integration with Simulink	Use Simulink as an integration platform for design, simulation, and code generation of multiple software components.
Hardware Deployment with Simulink	Generate and deploy code directly from Simulink models to embedded computing systems.

Part 1: Controlling Robot Motion	Control a robot to move on its wheels autonomously using dead reckoning.
Part 2: Using PID Controllers	Design and tune a PID controller to perform navigation tasks like dead reckoning.
Part 3: Designing Line Following Algorithms	Design line following algorithms for a mobile robot.
Part 4: Designing Obstacle Detection Algorithms	Design obstacle detection algorithms for a mobile robot.
Part 5: Performing a Sequence of Path Navigation Tasks	Create a supervisory logic that navigates a robot through a predefined path.

Part 1: Introduction to Simscape	Explore concepts of plant modeling with Simscape and the physical network approach. Using a battery model, learn how to build and simulate a model in Simscape.
Part 2: Simscape Fundamentals	Learn fundamental concepts of Simulink like using foundation libraries, creating multidomain physical components, dividing components into subsystems, and setting initial conditions for physical variables.
Part 3: Introduction to Vehicle Modeling	Gain an understanding of vehicle modeling, including how to model vehicle bodies, tires, brakes, and how to incorporate wind and terrain effects. This tutorial is applicable for both combustion and electric engine student competition teams.
Part 4: Powertrain Modeling	Learn about powertrain modeling and how to actuate vehicle models with power sources, build driveline mechanisms, create multi-speed transmissions, and model engines.
Part 5: Vehicle Drive and Control	Explore vehicle drive and basic control concepts, including implementation of a DC motor drive mechanism, PWM (Pulse Width Modulation) actuation, closed-loop control of the vehicle, and running simulations with imported drive cycle data.
Part 6: Introduction to Multibody Simulation	Discover the concept of multibody modeling with Simscape Multibody (formerly SimMechanics)Simscape Multibody extends Simscape with the ability to easily model rigid body mechanical systems in 2D and 3D.
Part 7: Building Components	Create assembly components, including simple geometries, extruded and revolved solids, and compound bodies, in Simscape Multibody (formerly SimMechanics). The components of a suspension system are used as an example
Part 8: Building Mechanical Assemblies, Section 1	Build an assembly in Simscape Multibody (formerly SimMechanics). See how to implement coordinate transforms, represent degrees of freedom, and specify body interfaces for reusability.
Part 9: Building Mechanical Assemblies, Section 2	Building on the example from “Part 8: Building Mechanical Assemblies, Section 1”, learn how to sense and log simulation results and add internal mechanics to joints.
Part 10: Importing CAD Models into SimMechanics	Visualize bodies with CAD geometries, export models from CAD software, and import CAD models into Simscape Multibody (formerly SimMechanics) for dynamic simulations..
Part 11: Design Optimization	Learn what Simulink Design Optimization is, as well as how to select and design parameters, set requirements or design goals, and optimize model parameters.

Importing and Visualizing Videos	Import and visualize video from a file or camera.
Object Detection Using Blob Analysis	Detect objects using blob analysis, and in doing so, learn how to perform color space conversion, thresholding, morphological operations, and video annotation.
Matching Image Features	Detect and match features between images.
Automating Image Registration	Estimate geometric transforms, apply the transform, and specify image coordinates using panoramic stitching as an example.
Object Detection: Part 1	Detect objects using template matching, histogram of gradients (HOG), and cascade object detectors.
Object Detection: Part 2	Detect moving objects using Foreground Detection and recognize text using OCR.
Motion Estimation	Estimate motion between video frames using Optical Flow.
Object Tracking	Track an object across video frames using histogram-based tracking and Kalman filters.
Line Detection	Detect lines using Hough transform in MATLAB.
Point Clouds	Understand point clouds and how to work with them in MATLAB.
Stereo Vision	Learn about stereo vision and calibrating stereo cameras. An example of reconstructing a scene using stereo vision is used.

Motor Control Design with MATLAB and Simulink	Identify core pieces of a field-oriented controller in a Simulink model, and learn how to autotune PI controller gains. Distinguish between dynamic decoupling control and flux weakening control.
Modeling Batteries Using Simulink and Simscape	Learn about equivalent circuits and why you’d want to use them. Use equivalent circuits to represent the dynamic behavior of a battery cell.
Why Model and Simulate HEVs	Identify the challenges associated with HEV design and with architecture selection. Understand energy consumption and performance estimates over different drive cycles and identify the impact of component selection.
Creating HEV Plant Models	Learn about different methods for creating HEV component models. See how Powertrain Blockset and Simscape tools can be used for HEV modeling, and learn best practices for creating new plant models.
Developing HEV Control Systems	Get an overview of HEV control systems and the concept of energy management. Understand control algorithm implementation in Simulink and Stateflow, test your controller, and learn best practices.
Optimizing HEV Models	Get an introduction to optimization and learn about MATLAB and Simulink optimization tools. Simultaneously optimize control and component parameters. Find a common set of control parameters for various driving conditions.
Battery Cell Balancing and State of Charge (SOC) Estimation	Learn about battery management system tasks. See how Simulink can model a physical plant and the controller for a battery pack. Identify how a nonlinear observer block from the controls library can keep track of the state of charge of a cell.
Battery Thermal Management System Design	Explore the components of a battery thermal management system for a small 4-passenger EV. Examine a Simscape model for this system, and use the model to diagnose and correct a problem with the control algorithm and investigate energy usage.