29.【Visualization】Understanding P-Control Gain Tuning Through Animation

tags: [“Control Theory”, “PID Control”, “P Control”, “Visualization”, “Python”]

The “I Think I Get It” Problem with P-Gain Tuning ⚠️

In textbooks, P control is usually explained like this:

• Increasing Kp makes the response faster
• Increasing it too much causes oscillation and instability

While this is logically correct,
it is honestly hard to grasp intuitively how the response evolves over time.

🎞 Comparing Responses Simultaneously with Animation

To address this, the step response of P control was visualized
by changing only Kp and animating the results side by side.

What to Pay Attention To

• Same plant
• Same target value (step input)
• Only Kp is changed

👉 Because the three responses
👉 progress along the same time axis simultaneously,

you can immediately see differences in:

• Rise time
• Overshoot
• Oscillation behavior

🧩 GIF Generation Code (Excerpt)

In this GIF,
the simulation is run by keeping all conditions the same and switching only the gain.

responses = [simulate_p(Kp) for Kp in Kp_list]

This single line does the following 👇

• Same plant model
• Same target value
• Only Kp is varied
• Each response is drawn simultaneously as it evolves over time

📌 Things that are hard to convey with static plots—
📌 such as “where it starts to become dangerous”—
become clearly visible through motion.

📎 Python Code Used

The full code used to generate the GIF is available here 👇

• P-control step response animation
  https://github.com/Samizo-AITL/qiita-articles/blob/main/demos/gif_anim/p_step_only.py

Running it will generate
the exact same GIF used in this article.

✨ Summary

• Increasing Kp makes the response faster
• But it also increases oscillation
• Increasing it further pushes the system toward instability

This continuous progression of change
is immediately clear when viewed as an animation.

🧠 Closing Thoughts

Both semiconductor physics and control theory
are fundamentally dynamic phenomena that depend on time and conditions.

Animation serves as
a powerful set of training wheels 🚲
to support intuitive understanding of these systems.