Tutorial: Using Fixed-Point Parameters and Local Data

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Goal of the Tutorial Building the Fixed-Point Butterworth Filter Defining the Model Callback Function Adding Other Blocks to the Model Setting Configuration Parameters for the Model Running the Model

Goal of the Tutorial

In the sections that follow, you build a model that uses fixed-point parameters and local data in a Stateflow chart. In this model, the chart acts as a low-pass Butterworth filter:

Building this model requires a Signal Processing Toolbox™ license.

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Building the Fixed-Point Butterworth Filter

In this section, you create a stateless flow chart that accepts one input and provides one output.

1. At the MATLAB prompt, type sfnew to create a new model with an empty chart.

2. In your chart, add a flow graph with a single branch:

   

   The values b0, b1, and a1 are the coefficients of the low-pass Butterworth filter. For more information about the filter coefficients, see Defining the Model Callback Function.

3. Add the following data to your chart:

   Data Name	Scope	Type
   x	Input	Inherit:Same as Simulink
   y	Output	fixdt(1,16,10)
   x_n1	Local	fixdt(1,16,12)
   y_n1	Local	fixdt(1,16,10)
   b0	Parameter	fixdt(1,16,15)
   b1	Parameter	fixdt(1,16,15)
   a1	Parameter	fixdt(1,16,15)

4. Save your model.

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Defining the Model Callback Function

In this section, you define a preload callback for the model. This callback function computes the values for b0, b1, and a1 in the chart.

1. Open the Model Properties dialog box by selecting File > Model Properties in the model window.

2. In the Callbacks tab, select PreLoadFcn.

3. Enter the following MATLAB code for the preload function:

   Fs = 1000;
   Fc = 50;
   [B,A] = butter(1,2*pi*Fc/(Fs/2));
   b0 = B(1);
   b1 = B(2);
   a1 = A(2);

   In the code:

   • The sampling frequency Fs is 1000 Hz.

   • The cutoff frequency Fc is 50 Hz.

   • The butter function constructs a first-order low-pass Butterworth filter with a normalized cutoff frequency of (2*pi*Fc/(Fs/2)) radians per second. The function output B contains the numerator coefficients of the filter in descending powers of z. The function output A contains the denominator coefficients of the filter in descending powers of z.

4. Click OK to close the dialog box.

5. Save your model.

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Adding Other Blocks to the Model

In this section, you add the remaining blocks to the model.

1. Open the Simulink Library Browser.

2. From the Simulink/Sources library, add a Sine Wave block with the following parameter settings to the model:

   Parameter	Setting
   Sine type	Time based
   Time	Use simulation time
   Amplitude	1
   Bias	0
   Frequency	2*pi*Fc
   Phase	0
   Sample time	1/Fs
   Interpret vector parameters as 1-D	On

   The Sine Wave block provides the signal that you want to filter using the Stateflow chart. This block outputs a floating-point signal.

3. From the Simulink/Signal Attributes library, add a Data Type Conversion block with the following parameter settings to the model:

   Parameter	Setting
   Output minimum	[]
   Output maximum	[]
   Output data type	fixdt(1,16,14)
   Lock output data type setting against changes by the fixed-point tools	Off
   Input and output to have equal	Real World Value (RWV)
   Integer rounding mode	Floor
   Saturate on integer overflow	Off
   Sample time	-1

   The Data Type Conversion block converts the floating-point signal from the Sine Wave block to a fixed-point signal. By converting the signal to a fixed-point type, the model can simulate using less memory.

4. From the Simulink/Sinks library, add a Scope block to the model.

5. Connect and label the blocks as follows:

   

6. Close the Library Browser and save your model.

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Setting Configuration Parameters for the Model

In this section, you specify solver and diagnostic options for simulation.

1. Open the Configuration Parameters dialog box.

2. In the Solver pane, set the following parameters:

   Parameter	Setting
   Stop time	0.1
   Type	Fixed-step
   Solver	discrete (no continuous states)
   Fixed-step size (fundamental sample time)	1/Fs

   Because none of the blocks in your model have a continuous sample time, a discrete solver is appropriate. For more information, see Solver Pane in the Simulink Graphical User Interface documentation.

3. In the Diagnostics > Data Validity pane, set the following parameters:

   Parameter	Setting
   Signals > Signal resolution	Explicit and warn implicit
   Parameters > Detect precision loss	none

   By setting the diagnostic settings for data validity, you control what types of warnings or errors appear during simulation. For more information, see Diagnostics Pane: Data Validity in the Simulink Graphical User Interface documentation.

4. Click OK to close the dialog box.

5. Save and close your model.

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Running the Model

When you reopen and simulate the model, you see these results in the scope:

The top signal shows the fixed-point version of the sine wave input to the chart. The bottom signal corresponds to the filtered output from the chart. The filter removes high-frequency values from the signal but allows low-frequency values to pass through the chart unchanged.

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Tutorial: Using Fixed-Point Chart Inputs

Operations with Fixed-Point Data

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