MATLAB and Simulink Training

Course Details

This three-day course, targeted toward new users of Simulink®, uses basic modeling techniques and tools to demonstrate how to develop Simulink block diagrams for signal processing applications.
Topics include:

  • What is Simulink?
  • Using the Simulink interface
  • Modeling single-channel and multi-channel discrete dynamic systems
  • Implementing sample-based and frame-based processing
  • Modeling mixed-signal (hybrid) systems
  • Developing custom blocks and libraries
  • Modeling condition-based systems
  • Performing spectral analysis with Simulink
  • Integrating filter designs into Simulink
  • Modeling multirate systems
  • Incorporating external code
  • Automating modeling tasks

Day 1 of 3

What is Simulink?

Objective: Get an introduction to Simulink.

  • System Design Process
  • Model-Based Design with Simulink
  • What Can You Do with Simulink?
  • Simulink add-ons

Creating and Simulating a Model

Objective: Explore the Simulink interface and block libraries. Build a simple model and analyze the simulation results.

  • Creating and editing a Simulink model
  • Defining system inputs and outputs
  • Simulating the model and analyzing results
  • Performing automatic initialization of Simulink model parameters
  • Visualizing signals with signal viewers

Modeling Discrete Dynamic Systems

Objective: Model discrete dynamic systems, and visualize frame-based signals and multichannel signals using a scope.

  • Modeling a discrete system with basic blocks
  • Finding sample times of block outputs
  • Using frames in your model
  • Using buffers
  • Comparing frames vs. multichannel signals
  • Viewing frame-based signals
  • Understanding behavior of delay blocks with frame-based signals
  • Working with multichannel frame-based signals

Modeling Logical Constructs

Objective: Model logical expressions. See how zero-crossing detection is used in Simulink and model simple logic in Simulink using MATLAB code.

  • Modeling logical expressions
  • Modeling conditional signal routing
  • Understanding zero-crossing detection
  • Modeling with the MATLAB Function block

From Algorithm to Model

Objective: Create a model from an algorithm specification.

  • Modeling from algorithmic specifications
  • Controlling model behavior under some error conditions
  • Iterative algorithm development through modeling and simulation
  • Verifying models against specified algorithms

Day 2 of 3

Mixed-Signal Models

Objective: Model mixed-signal systems.

  • What is a mixed-signal model?
  • Modeling an analog-to-digital Converter (ADC) with aperture jitter and nonlinearity
  • Case study: Modeling TI's ADS62P29 ADC

Solver Selection

Objective: Choose the right solver for a Simulink model.

  • Understanding the Simulink solver
  • Solving simple models
  • Solving models with discrete and continuous states
  • Solving models with multiple rates
  • Fixed-step and variable-step solvers
  • Choosing a continuous-state system solver
  • Handling zero crossings
  • Handling algebraic loops

Subsystems and Libraries

Objective: Create custom blocks in Simulink, apply masks, and develop custom libraries.

  • Creating subsystems
  • Understanding virtual and atomic subsystems
  • Using a subsystem as a model component
  • Masking subsystems
  • Creating custom block libraries
  • Working with and modifying library blocks
  • Adding custom libraries to the Simulink Library Browser

Conditional Subsystems

Objective: Model systems with parts that are executed conditionally.

  • Modeling conditionally executed subsystems
  • Creating enabled subsystems
  • Creating triggered subsystems
  • Working with an example using the AGC model

Spectral Analysis

Objective: Perform spectral analysis in the Simulink environment, and use spectrum computation in an algorithm.

  • Performing spectral analysis with the Spectrum Analyzer block
  • Choosing spectral analysis parameters
  • Analyzing power spectrum of a fan motor noise
  • Building a spectral classifier for speech
  • Determining frequency response of a discrete system

Day 3 of 3

Designing and Applying Filters

Objective: Incorporate filters in a model, and explore different ways filters can be designed and implemented in a Simulink model.

  • Designing filters in Simulink
  • Modeling filters in fixed-point

Multirate Systems

Objective: Model multirate systems. Resample data and explore multirate filter blocks.

  • Modeling multirate systems
  • Exploring blocks for multirate signal processing
  • Resampling oversampled data
  • Designing and implementing anti-imaging and anti-aliasing filters
  • Using multirate filter blocks
  • Case study: Converting professional audio to CD format
  • Converting the design to fixed point

Incorporating External Code

Objective: Import or incorporate custom or external MATLAB and C code into a Simulink model.

  • Working with custom and external code
  • Incorporating MATLAB code with the MATLAB Function block
  • Incorporating C code with the C Caller block

Combining Models into Diagrams

Objective: Explore model integration, an important topic for large-scale projects in which several developers are developing different portions of a large system.

  • Exploring model referencing and subsystems
  • Setting up a model reference
  • Setting up model reference arguments
  • Exploring model reference simulation modes
  • Viewing signals in referenced models
  • Browsing model reference dependency graph

Automating Modeling Tasks

Objective: Control and run Simulink models from the MATLAB command line.

  • Automating test runs
  • Checking and modifying parameter settings
  • Finding blocks with specific parameter values
  • Constructing and modifying block diagrams

Level: Intermediate


Duration: 3 days

Languages: English, 한국어

View schedule and enroll