Process Message Payload Using MATLAB System Block
This example shows how to send, receive, and process messages using the MATLAB System block. Use System objects to author blocks to model custom behavior to send and receive messages and manipulate the message payload.
Load and Open the Model
Open the model of the Message Send and Receive system. This model contains a random number generator as a data source. Based on that data, the Message Sender sends a message with a sine wave payload to a queue block. The queue block stores the messages, and the Message Receiver converts the message back to data.
getInterfaceImpl System Object API to Specify Message Ports
getInterfaceImpl is used in this example to specify the input and output message ports in the MATLAB System block for both the Message Sender and the Message Receiver. For the Message Sender,
getInterfaceImpl is used to define the output of the MATLAB System block as a message. This action prompts the System object to create a message output. For the Message Receiver,
getInterfaceImpl is used to define the input of the System object as a message and the output as data.
%Function to send messages function interface = getInterfaceImpl(~) import matlab.system.interface.*; interface = Output("Out1", Message); end
%Function to receive messages and output as data function interface = getInterfaceImpl(obj) import matlab.system.interface.*; interface = [Input("In1", Message), ... Output("Out1", Data), Output("out2", Data)]; end
Set the Propagators and Sample time
The following four propagators need to be set in the Message Sender:
In this example, the message queue has a maximum capacity of 16 messages. The random number generator has a sample time of 0.1. The receiver has a sample time of 1. The Message Receiver sample time is set in the MATLAB System block using the
function sts = getSampleTimeImpl(obj) sts = createSampleTime(obj,'Type','Discrete', ... 'SampleTime',obj.SampleTime); end
Simulate the Model and Review Results
The Scope block displays the results. These results show that as you run more simulations, the random number generator produces a number greater than zero 50% of the time, as expected.