# Impedance Gain Tuning

> Tune or diagnose robot hand/arm kp/kd, PD/PID, and impedance responses once geometry and IK are trustworthy.

- Skill: `c1trusovo831/impedance-gain-tuning` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add c1trusovo831/impedance-gain-tuning`
- Raw SKILL.md: https://api.skillmd.com/api/skills/c1trusovo831/impedance-gain-tuning/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: C1TRuSovo831 (https://skillmd.com/u/c1trusovo831)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/c1trusovo831/impedance-gain-tuning

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# Impedance and gain tuning

Use this skill after geometry and IK are stable enough that a control response is meaningful. It covers software outer loops, hand/arm command shaping and impedance behavior; it does not assume that the vendor SDK exposes the same loop or units.

## Identify the actual loop first

Record the active checkout and configuration, control layer (position, velocity or torque), vendor inner-loop gains, command and feedback units, sample period `dt`, delay, filtering, saturation, rate limits and watchdog behavior. Check whether a derivative term uses measured velocity; differentiating a setpoint step can cause derivative kick. Do not tune an outer PID on top of an unknown inner PID.

For a torque impedance loop, use the model as a reference:

`tau = Kp * (qd - q) + Kd * (qd_dot - q_dot) + tau_ff`.

The usual units are `N m/rad` and `N m s/rad`, subject to the drive's actual interface. An estimate such as `Kd ≈ 2*zeta*sqrt(M*Kp)` is only an initial value; identify or bound the effective inertia `M` and then measure the response.

## Tuning procedure

1. Establish a safe offline, replay, simulator or fake baseline with identical input, timing and limits.
2. Tune an inner loop before an outer loop. Within one loop, change one variable at a time; do not use larger `Kp` to hide retargeting or IK errors.
3. Sweep `Kp` over a bounded range, then `Kd` and derivative filtering. Keep position, velocity, acceleration, jerk, torque/current and workspace limits active.
4. Evaluate step and representative trajectory responses using rise time, settling time, overshoot, steady-state error, RMS tracking error, oscillation spectrum, delay, saturation rate and jerk.
5. Keep a reversible parameter record and rollback point. Stop on instability, repeated saturation, watchdog trips or limit violations.
6. Only after the offline/fake result is stable should a separately authorized HIL review consider a physical run.

Report whether each result is source, static, replay/simulation, runtime, HIL or physical-safety evidence. Never present a gain sweep or a stable simulator response as proof that a real robot is safe.