how to limit random number in plc

3 min read 19-01-2025

PLCs (Programmable Logic Controllers) are frequently used in applications requiring random number generation, such as simulations, testing, or introducing variability into control processes. However, often you need to constrain these random numbers within a specific range. This article details several methods to limit random numbers generated within a PLC program, focusing on practical examples and considerations for different PLC programming languages.

Understanding PLC Random Number Generation

Before diving into limiting the numbers, let's briefly review how random numbers are typically generated in PLCs. Most PLCs offer built-in functions or instructions to generate pseudo-random numbers. These aren't truly random (they are deterministic sequences based on a seed value), but for many applications, they provide sufficient randomness. The output is usually a relatively large integer.

Methods for Limiting Random Numbers

The core challenge is to take this potentially large, seemingly random integer and map it to a smaller, desired range. Here are common techniques:

1. The Modulo Operator (%)

This is the simplest and often most efficient method. The modulo operator provides the remainder after division. For example, to limit a random number randomNumber to the range 0-9, you'd use:

limitedNumber := randomNumber MOD 10;

This works because the remainder will always be between 0 and 9 (the divisor minus 1). However, the distribution might not be perfectly uniform if the range of randomNumber isn't a multiple of 10.

Example (Ladder Logic):

Imagine you have a random number generated in a PLC register called Random_Number. To limit it to a range of 0-4, you would use the modulo operator with a constant 5:

[Diagram showing ladder logic with a random number register, a modulo instruction with a constant 5, and the resulting limited number in an output register. This would need to be a visual representation, unfortunately I cannot create images.]

2. Scaling and Shifting

This provides more control and ensures a uniform distribution within the desired range. First, you scale the random number to the desired range's size, then shift it to the correct starting point.

Let's say randomNumber is in the range 0-1000, and you need to limit it to 10-20.

Scaling: scaledNumber := (randomNumber / 1000) * (20 - 10); This scales the number to the size of the target range (10).
Shifting: limitedNumber := scaledNumber + 10; This adds the starting value of the target range.

This method maintains a more even distribution across the range.

Example (Structured Text):

randomNumber := RandomInteger(1000); // Assumes a function to generate random integers up to 1000
minRange := 10;
maxRange := 20;

scaledNumber := (randomNumber / 1000) * (maxRange - minRange);
limitedNumber := scaledNumber + minRange;

3. Using PLC-Specific Functions

Many PLCs have built-in functions for generating random numbers within a specified range. Consult your PLC's programming manual for details. These often handle the scaling and shifting internally, simplifying the process. For example, some PLCs offer a function like RandRange(min, max) that directly returns a random number within the given bounds.

4. Handling Negative Numbers

If your random number generation can produce negative values, you'll need to adapt the methods. You might need to take the absolute value before applying the modulo or scaling method. Consider adding an offset to shift the negative range into the positive before processing.

Important Considerations

Seed Value: The initial seed value for the random number generator impacts the sequence. For repeatable testing, initialize the seed with a known value. For true randomness (if needed), use an external source like a hardware random number generator or system time.
Distribution: The modulo method can lead to slightly uneven distributions for certain ranges. Scaling and shifting or built-in functions generally provide better uniformity.
PLC Resources: Consider the computational overhead of different methods, especially in resource-constrained PLCs. The modulo operation is generally the least demanding.

By understanding these techniques, you can effectively limit random numbers in your PLC programs, adding variability and flexibility to your automation solutions. Remember to always consult your PLC's documentation for specific functions and syntax.