Helicopter: A 2D Height Control Simulation

Simplifying PID Controller Concepts for Engineers and Students

The PID (Proportional-Integral-Derivative) controller is a staple in control engineering, known for its simplicity and effectiveness. However, its technical jargon can be daunting for beginners. This guide is crafted to demystify PID controllers, making them accessible for engineering students and early-career engineers.

Environment Preparation
Observing Virtual Helicopter Information
Implementing a PID Controller
Conclusion

1. Environment Preparation

Begin by setting up your environment to simulate a virtual helicopter.

1.a Clone 2D Helicopter Repository

Clone the necessary repository with this command:

bash

Line #:

git clone https://github.com/ubicoders/vrobots_heli

1.b Install Required Pip Package

Install the required Python package:

bash

Line #:

pip install ubicoders-vrobots

1.c Run Virtual Robot Bridge

Start the virtual robot bridge:

bash

Line #:

python -c "import ubicoders_vrobots.vrobots_bridge.vrobots_bridge_gui as gui; gui.gui_app_run()"

1.d Run the Demo Script

Execute the demo script:

bash

Line #:

python my_helil.py

The script structure:

bash

Line #:

from ubicoders_vrobots import System, Helicopter

heli = Helicopter()

class UbicodersMain:
    def __init__(self) -> None:
        self.error_sum = 0
    
    def setup(self):
        pass

    def loop(self):
        if heli.states == None:
            return
        
        states = heli.states
        print(states)

        error = 5 - heli.states.pos.z
        self.error_sum += error

        heli.set_force(error*5 - heli.states.vel.z*5 + self.error_sum*0.08) # Newton

if __name__ == "__main__":
    sys = System(heli, UbicodersMain())
    sys.start()

Code Insights

The UbicodersMain class, with its functions __init__, setup, and loop, orchestrates the simulation. The loop() function, in particular, is pivotal in controlling the helicopter's thrust by manipulating the states variable.

2. Observing Virtual Helicopter Information

Helicopter Simulation Output Virtual helicopter's position and velocity data.

The heli.states output provides crucial information such as the helicopter's height and vertical velocity.

Helicopter Simulation Target Target height for the helicopter in the simulation.

The objective is to align the helicopter with the green bar, which is at a height of 5 meters.

3. Implementing a PID Controller

The aim is to maintain the helicopter at a 5-meter altitude.

Force Calculation

The force exerted by the helicopter is based on the "error" - the difference between the target and current heights:

$$ E = target - h_{current} $$

In Python:

bash

Line #:

error = 5 - heli.states.pos.z

The updated force equation: $$ force = error \times 5 - vel \times 5 $$

Python implementation:

bash

Line #:

heli.set_force(error*5 - heli.states.vel.z*5)

Experimenting with different scale factors can lead to varied system responses.

Addressing Steady State Error

To correct persistent errors, the error over time is integrated:

$$ force = error \times 5 - vel \times 5 + \int{error} $$

Python code:

bash

Line #:

self.error_sum += error
heli.set_force(error*5 - heli.states.vel.z*5 + self.error_sum*0.08)

Run the script to observe the helicopter maintaining its height at the target.

Helicopter Maintaining Height Helicopter simulation maintaining height at the target level.

Entire Code

bash

Line #:

from ubicoders_vrobots import System, Helicopter

# Create a helicopter object that contains states (position and velocity)
heli = Helicopter()

class UbicodersMain:
    def __init__(self) -> None:
        # Define the variables here!
        self.error_sum = 0
    
    def setup(self):
        pass

    def loop(self):
        if heli.states == None:
            return
        
        # Print the helicopter states from the simulator!
        states = heli.states
        print(states)

        error = 5 - heli.states.pos.z
        self.error_sum += error

        # Set the thrust force to the helicopter!
        heli.set_force(error*5 - heli.states.vel.z*5 + self.error_sum*0.08) # Newton

if __name__ == "__main__":
    sys = System(heli, UbicodersMain())
    sys.start()

4. Conclusion

PID controllers, while initially appearing complex, can be mastered with a basic grasp of their underlying principles. This guide, through a practical virtual helicopter simulation, demonstrates how PID controllers can be implemented effectively. For engineering students and budding engineers, understanding and applying these concepts in simulations paves the way for more advanced applications in control engineering.

Your feedback and experiences with the simulation are invaluable. Share your thoughts and findings in the comments below, and let's foster a collaborative learning environment!

Ubicoders - skill up coding skills ubiquitously!

Helicopter: A 2D Height Control Simulation

No Human Can Play This Game Maintain the Height of a 2D Helicopter by PID Controller in Python.

Simplifying PID Controller Concepts for Engineers and Students

Table of Contents

1. Environment Preparation

1.a Clone 2D Helicopter Repository

1.b Install Required Pip Package

1.c Run Virtual Robot Bridge

1.d Run the Demo Script

Code Insights

2. Observing Virtual Helicopter Information

3. Implementing a PID Controller

Force Calculation

Addressing Steady State Error

4. Conclusion

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