Description

Simulink is the model-based design that supports system-level design, automatic code generation, continuous test and verification of embedded systems.

Course Details

This course (formerly known as Simulink for System and Algorithm Modeling) is for engineers new to system and algorithm modeling in Simulink. It teaches attendees how to apply basic modeling techniques and tools to develop Simulink block diagrams.

Topics include :

a). Creating and modifying Simulink models and simulating system dynamics

b). Modeling continuous-time, discrete-time, and hybrid systems

c). Modifying solver settings for simulation accuracy and speed

d). Building hierarchy into a Simulink model

e). Creating reusable model components using subsystems, libraries.

f). If your application is signal processing or communications, please refer to the Signal Processing.

1. Creating and Simulating a Model

Objective: Create a simple Simulink model, simulate it, and analyze the results.

a). Introduction to the Simulink interface

b). Potentiometer system

c). System inputs and outputs

d). Simulation and analysis

2. Modeling Programming Constructs

Objective: Model and simulate basic programming constructs in Simulink.

a). Comparisons and decision statements

b). Vector signals

c). PWM conversion system

d). Zero crossings

e). MATLAB Function block

3. Modeling Discrete Systems

Objective: Model and simulate discrete systems in Simulink.

a). Discrete signals and states

b). PI controller system

c). Discrete transfer functions and state-space systems

d). Multirate discrete systems

4. Modeling Continuous Systems

Objective: Model and simulate continuous systems in Simulink.

a). Continuous states

b). Throttle system

c). Continuous transfer functions and state-space systems

d). Physical boundaries

5. Solver Selection

Objective: Select a solver that is appropriate for a given model.

a). Solver behavior

b). System dynamics

c). Discontinuities

d). Algebraic loops

6. Developing Model Hierarchy

Objective: Use subsystems to combine smaller systems into larger systems.

a). Subsystems

b). Bus signals

7. Modeling Conditionally Executed Algorithms

Objective: Create Subsystems that are executed based on a control signal input.

a). Conditionally executed Subsystems

b). Enabled Subsystems

c). Triggered Subsystems

d). Input Validation model

8. Combining Models into Diagrams

Objective: Use model Referencing to combine models.

a). Subsystems and Model Referencing

b). Model Referencing Workflow

c). Model Reference Simulation modes

d). Model Work spaces

e). Model Dependencies

9. Creating Libraries

Objective: Use Libraries to create and Distribute custom blocks.

a). Creating and Populating Libraries