Variable Pulse Generator
Generate ideal, time varying pulse signal
 Library:
Simulink / Discontinuities
Description
Use the Variable Pulse Generator block to create ideal modulated pulse signals.
Generally speaking, the output pulse of the block is described by
$$y(t)=\{\begin{array}{cc}1& {t}_{k}<t<{t}_{k}+pw\\ 0& {t}_{k+1}<t<{t}_{k}+pw\end{array}$$
where pw is the output pulse width.
For an implementation of Pulse Width Modulation, see PWM.
Ports
Input
D
— Duty Cycle
scalar
Desired duty cycle of the pulse P, specified as scalar within the range [0,1].
Data Types: double
P
— Period
scalar
Time between rising edges of consecutive pulses of the output signal. A smaller value represents a higher frequency pulse.
Data Types: double
Output
Port 1
— Modulated pulse signal
scalar
Modulated output pulse signal corresponding to input duty cycle.
Data Types: double
Parameters
Allow zero pulse width
— Allow zero magnitude of output signal
off (default)  on
Enable this parameter to allow the output pulse signal to support pulses of width 0.
Note
Enabling this parameter causes the block to have direct feedthrough. This can cause algebraic loops in your model.
Data Types: Boolean
Run at fixed time intervals
— Choose continuous or discretetime behavior
continuous
(default) 
discrete
Select whether the block should operate in continuous or discrete sampling modes.
By default, the block uses continuous
sampling mode as it
improves simulation performance with variable step solvers.
Select discrete
sampling mode if you need to:
use a fixedstep solver
generate code
sample the block output
Sampling rate
— Set pulse resolution
0.1 (default)  scalar
Specify the rate at which the block samples input duty cycle signal. This sampling rate becomes the resolution of the output pulse signal.
Dependencies
This parameter requires that Sampling mode is set to
discrete
Block Characteristics
Data Types 

Direct Feedthrough 

Multidimensional Signals 

VariableSize Signals 

ZeroCrossing Detection 

Algorithms
Continuous Sampling Mode
For a pulse starting at time t_{k}
$$y(t)=\{\begin{array}{cc}1& {t}_{k}<t<{t}_{k}+pw\\ 0& {t}_{k+1}<t<{t}_{k}+pw\end{array}$$
where pw is the pulse width. For a given period P, pw is proportional to the duty cycle D
$$pw=D({t}_{k})*P({t}_{k})$$
Discrete Sampling Mode
In Discrete sampling mode, the input duty cycle signal is sampled at the rate specified by the Run at fixed time intervals parameter.
For a specified sampling rate t_{S} , the number of samples needed for a pulse of width pw can be expressed as follows
$$\begin{array}{l}{n}_{pw}=\begin{array}{cc}\lfloor \frac{{D}_{k}.{P}_{k}}{{T}_{S}}\rfloor & 0<{n}_{pw}<{n}_{P}\end{array}\\ {n}_{P}=\lfloor \frac{P}{{T}_{S}}\rfloor \end{array}$$
where n_{P} is the number of samples needed to simulate a pulse of period P.
Consider a nominal pulse of period P with the sampling rate of the block set to be t_{S}= 0.25 P. The number of samples needed for one period of the pulse, n_{P}= 4. Thus, for the input duty cycle D= 0.47 , the number of samples n_{ pw} is floored to $$\lfloor \frac{0.47P}{0.25}\rfloor $$= 1. Therefore, the pulse is high for 1 of the 4 samples in the period.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.
Not recommended for productionquality code. Relates to resource limits and restrictions on speed and memory often found in embedded systems. The code generated can contain dynamic allocation and freeing of memory, recursion, additional memory overhead, and widelyvarying execution times. While the code is functionally valid and generally acceptable in resourcerich environments, smaller embedded targets often cannot support such code.
In general, consider using the Simulink Model Discretizer to map continuous blocks into discrete equivalents that support production code generation. To start the Model Discretizer, in the Simulink^{®} Editor, on the Apps tab, under Apps, under Control Systems, click Model Discretizer. One exception is the SecondOrder Integrator block because, for this block, the Model Discretizer produces an approximate discretization.
Version History
Introduced in R2020b
See Also
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