Active Flux Pinning Dynamics (AFPD)

Hybrid Classical + Quantum + Nonlinear Control Model

With Dual-Input Actuation, Nonlinear μ(v), and F_inject

This repository contains the full implementation of Active Flux Pinning Dynamics, a hybrid control architecture combining:

• Classical suspension dynamics
• Quantum-inspired pinning stiffness and damping
• Nonlinear damping μ(v)
• Dual-input stiffness + damping control
• Active injection term F_inject
• Bayesian Optimization fine‑tuning
• Micro‑burst damping
• Stability‑bounded Ts‑priority search

The system is built to push a 1‑DOF toy model as close as possible to a triple‑metric win over passive quantum suspension:

System	Settling Time	Overshoot	RMS Accel
Classical	❌ Worst	❌ Worst	❌ Worst
Passive Quantum	Baseline	Baseline	Baseline
AFPD (tuned)	Better	Better	Better

The full paper (active_flux_pinning_dynamics.md) describes the physics, math, and motivation behind the project.

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📌 Features

• Nonlinear stiffness control:
  ( k_{ ext{pin}}(t) ) driven by displacement, velocity, and actuator dynamics.

• Nonlinear damping μ(v):
  ( \mu_{ ext{eff}} = \mu_0 + \eta_x |x| + \eta_v rac{|v|}{1+�eta_\mu |v|} )

• Active Injection Force:
  ( F_{ ext{inject}} = -k_F x - c_F v )

• Micro‑Burst Damping:
  Activated only during high‑frequency velocity spikes for rapid stabilization.

• Fully tunable Bayesian Optimization pipeline using scikit‑optimize.

• Fast stable integrator suitable for stiff, nonlinear 1‑DOF motion.

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📁 Repository Contents

Active_Flux_Pinning_Dynamics/
│
├── AFPD.py                      # Main simulation + tuning engine
├── active_flux_pinning_dynamics.md   # Full theory + paper
├── example_outputs/             # Plots & tuned metrics
│   ├── displacement.png
│   ├── acceleration.png
│   ├── k_pin.png
│   ├── mu_pin.png
│   ├── metrics.json
│   └── ...
└── README.md                    # You are here

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▶️ Running the Model

Default simulation:

python AFPD.py

Bayesian optimization tuning:

python AFPD.py --tune

Requires:

pip install scikit-optimize

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📊 Latest Tuned Results (Ts‑Priority, Expanded + Nonlinear + F_inject)

Settling Time (Ts):   0.6097   (better than passive)
Overshoot (Mp):       0.5742   (better than passive)
RMS Accel:            2.3684   (better than passive)
Peak Accel:           100.0

This run shows the model achieving the desired triple‑metric win over passive.

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🧪 Goals & Research Direction

• Extend from 1‑DOF toy model → full multi‑axis suspension
• Add thermal flux modulation
• Add vector‑field control for real flux tube geometry
• Train a reinforcement model to control stiffness & damping adaptively
• Integrate this into a full AFPD hardware demonstrator

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📝 License

Apache 2.0 — Free for use in research & engineering.

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🤝 Contributions

Pull requests welcome.
This project is actively expanding — from toy physics to real mechatronics.