I can give a complete, ready-to-implement BRINTAF loop design and tuning procedure for normalizing the QV:7MALP prior to the druskel phase. I don’t know your exact hardware and timing constraints, so I’ll present a robust general design with recommended defaults and safety/fallback behavior; tell me any system specifics (sensor type, sampling rate, actuator latency, target normalization range, hard limits) and I’ll tailor numbers.

Summary
- Goal: drive QV:7MALP reading to target range Xtarget (default: zero-centered normalization ±ε) and hold it stable until druskel phase begins.
- Approach: nested feedback loop (fast inner actuator compensation + slower adaptive controller) with state estimation, gain scheduling, and fail-safe quench.
- Output: pseudocode, parameter suggestions, verification tests, and troubleshooting guidance.

Assumptions and components
- QV:7MALP is a scalar readout (can be generalized to vector).
- Sensor acquisition: synchronous sampling at fsample (choose 5–20× faster than the fastest QV dynamics).
- BRINTAF actuator supports continuous amplitude/phase adjustments; command latency τact known.
- druskel phase begins at tdruskel (or when an external trigger arrives).
- You have a supervisory controller that can disable BRINTAF and apply emergency quench.

Key design points
1. Two-layer control
   - Inner loop: high-frequency proportional-derivative (PD) compensation executed at sample rate to reject fast disturbances and compensate actuator latency.
   - Outer loop: slower integral/adaptive controller that removes steady-state bias and adapts gains based on sensor noise and system stability metrics.
2. State estimation
   - Use a lightweight Kalman or exponential filter to smooth readings and estimate drift.
3. Pre-druskel ramp
   - Gradually bring BRINTAF commands to full normalization authority to avoid overshoot when druskel phase starts.
4. Safety
   - Monitor stability metrics (variance, rate-of-change). If oscillation or divergence detected, trigger safe fallback: hold last safe command, reduce gain, or quench.

Control law (conceptual)
- Let q[k] = measured QV:7MALP at timestep k.
- q_est[k] = filtered estimate.
- error e[k] = q_est[k] − Xtarget.
- u_inner[k] = Kp(k) * e[k] + Kd(k) * (e[k] − e[k−1]) / Ts.
- u_outer[k] += Ki * e[k] * Ts (with anti-windup).
- u_total[k] = saturate(u_inner[k] + u_outer[k], umin, umax).
- Apply command to BRINTAF actuator with prediction compensation for τact (optionally use Smith predictor if τact significant).

Recommended default parameterization (adjust to system)
- Ts = 1/fsample. Choose fsample = 1–10 kHz for most fast-control BRINTAF systems; use faster if QV dynamics are fast.
- Kp initial = 0.4 / (system gain), Kd initial = 0.05*Ts-scaled, Ki initial = 0.1*Ts^-1 (slow).
- Anti-windup: clamp u_outer integration to ±(0.8*umax).
- Gain scheduling: reduce Kp by 50% if |e| > 3σnoise to reduce overshoot during large transients.
- Convergence criterion: |e| < ε_target for Nhold consecutive samples (e.g., ε_target = 0.02 units, Nhold = 500 samples).
- Safety thresholds: if |dq/dt| > dmax or measured PSD shows growing peak, reduce gains by 50% and re-evaluate. If instability persists > Trescue, quench.

Pseudocode (loop)
- Initialize filters, integrator, last_error.
- Precondition: run sensor baseline calibration for Tcal to estimate drift and noise σnoise.
- Ramp-in: for t in [0, Tramp] increase Kp_linearly from 0→Kp_init and max command amplitude from 0→umax_frac (e.g., 0.8) to avoid impulse at start.
- Main loop (every Ts):
  1. q = read_sensor()
  2. q_est = lowpass_filter(q)  (e.g., alpha = Ts/(τfilter+Ts))
  3. e = q_est − Xtarget
  4. derivative = (e − last_error)/Ts
  5. u_inner = Kp_current * e + Kd_current * derivative
  6. u_outer += Ki * e * Ts
  7. u_outer = clamp(u_outer, -uwind_max, uwind_max)
  8. u_total = clamp(u_inner + u_outer, umin, umax)
  9. apply_prediction_compensation_and_send(u_total)
 10. last_error = e
 11. update_adaptive_logic() — adjust gains slowly if error persistent or noise increases
 12. stability_check() — compute variance/PSD and trigger fallback if needed
 13. if convergence_criteria_met for Nhold samples: mark normalized and hold until druskel start

Prediction compensation
- If actuator latency τact significant relative to Ts and dynamics, use a Smith predictor:
  - Predict q[k+τact] using simple model (q + derivative*τact) and compute error against predicted target.

Validation and tuning procedure
1. Characterize open-loop response: apply small step and record QV response to estimate dominant time constant τ and gain G.
2. Set Ts ≤ τ/20, choose Kp ≈ 0.3/G, Ki ≈ 0.1/(τ*G), Kd ≈ 0.01*τ*G (start conservative).
3. Run closed-loop step with ramp_in disabled; measure overshoot and settling time. Reduce Kp if overshoot > 10%.
4. Run frequency sweep to check phase margin; adjust Kd and reduce Kp if margin < 45°.
5. Confirm convergence before druskel using the convergence criterion.

Diagnostics to log
- q, q_est, e, u_inner, u_outer, u_total, timestamps, detected anomalies, PSD snapshots. Keep at least pre-druskel window.

Fallback modes
- Soft-reduce: cut gains to 25% and attempt re-convergence.
- Hold mode: freeze u_total at last safe value and wait for operator action.
- Emergency quench: set actuator to safezero, alert operator and inhibit druskel start.

Example test sequence before live druskel
- Baseline capture: 10× τ to estimate noise.
- Ramp normalization with operator observation.
- 3 disturbance injections (small pulses) to verify rejection.
- Stability stress: apply worst-case known disturbance and verify safe fallback engages.
- Final pre-druskel: hold normalized state for Tmin_hold (recommend ≥ 5× τ) and only then arm druskel trigger.

Troubleshooting
- Persistent steady error: increase Ki slightly, or check for actuator saturation/clipping.
- Oscillation: reduce Kp and/or increase Kd; check actuator latency and model mismatch.
- Slow convergence: increase Ki, decrease filter time constant, or verify sensor bias/drift.
- High noise floor: tighten filter, slow outer loop, increase measurement averaging.

If you want, I can:
- Convert this pseudocode to runnable code in C, Python, or embedded pseudocode for your controller.
- Generate specific parameter values if you provide system identification data: G (gain), τ (time constant), τact (actuator latency), fsensor, and acceptable ε_target.

Tell me which controller platform and the QV:7MALP measurement units and I’ll produce the exact code and tuned parameters.
