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Robotics with Object Pascal

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Pascal PID

2024.12.24 15:06

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I searched for "pascal pid control sample" from google, and google answered with AI as follow.

AI Overview
Code
program PID_Control;

// Variable declarations
var 
  Setpoint: real;  // Desired value 
  ProcessValue: real;  // Measured value
  Error: real;  // Difference between setpoint and process value 
  Integral: real;  // Accumulated error
  Derivative: real;  // Rate of change of error
  Output: real;  // Control signal
  Kp, Ki, Kd: real;  // PID gains 
  PreviousError: real;  // Error from previous iteration 

procedure CalculatePID;
begin
  Error := Setpoint - ProcessValue;  // Calculate error
  Integral := Integral + Error; // Update integral term 
  Derivative := (Error - PreviousError); // Calculate derivative 
  Output := Kp * Error + Ki * Integral + Kd * Derivative; // Calculate control signal
  PreviousError := Error;  // Update previous error for next iteration
end; 

begin
  // Initialize PID gains
  Kp := 1; 
  Ki := 0.1; 
  Kd := 0.01;

  // Main loop
  repeat 
    // Read process value (replace with actual sensor reading)
    ProcessValue := ... ; 

    // Calculate PID output
    CalculatePID; 

    // Apply control signal (replace with actuator control)
    ... 
  until false; 
end.
 
Explanation:
  • Variables:
    • Setpoint: The desired value you want to maintain.
    • ProcessValue: The actual measured value from the system.
    • Error: The difference between the setpoint and the process value.
    • Integral: Accumulated error over time.
    • Derivative: Rate of change of the error.
    • Output: The control signal generated by the PID controller.
    • KpKiKd: Proportional, integral, and derivative gains respectively, which determine the influence of each term in the PID calculation.
    • PreviousError: Stores the previous error for derivative calculation.
  • CalculatePID procedure:
    • Calculates the error based on the current setpoint and process value.
    • Updates the integral term by adding the current error to the previous accumulated error.
    • Calculates the derivative by finding the difference between the current error and the previous error.
    • Calculates the control signal (Output) by summing up the proportional, integral, and derivative components weighted by their respective gains.
    • Updates the PreviousError for the next iteration.
 
Key points:
  • Tuning PID gains:
    The most important part of using a PID controller is properly tuning the KpKi, and Kd values to achieve the desired system response (stability, speed, accuracy).
  • Implementation details:
    The ... sections in the code represent placeholder for actual sensor readings and actuator control based on your specific system.
  • Discrete implementation:
    This example shows a discrete implementation of the PID algorithm, which is typically used in computer control systems.
  • Further considerations:
    • Anti-windup: To prevent excessive integral accumulation during saturation, you might need to implement an anti-windup mechanism.
    • Filtering: Filtering the derivative term can help reduce noise sensitivity.
    • Adaptation: Advanced PID controllers may include mechanisms to automatically adjust gains based on system conditions.
 
 
 
 
Generative AI is experimental.
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