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RQM Optimize Disturbance Rejection
x402
RQM Optimize Disturbance Rejection is a paid API for AI agents from jobs.rqmtechnologies.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-14).
Tunes PD controller gains (Kp, Kd) over a bounded grid search to minimize peak and RMS tracking error under a supplied disturbance signal for a mass-damper system.
Price / call$0.01
Success—
StatusUnknown
How this service works
Problem: Tune supported control parameters against this bounded disturbance model and supplied response objectives. Input: JSON with mass, damping, disturbance, time step seconds, baseline kp, baseline.... Result: candidate parameters, comparative metrics and constraint results. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.
Output
Returns the optimized Kp and Kd gain values found by the grid search, along with the achieved peak tracking error and RMS error under the supplied disturbance signal, and whether the result satisfies the caller's error constraints.
Example use cases
Robotic arm disturbance rejection tuning
“I'm building a robotic arm controller with a 2 kg link and 0.5 damping coefficient. I have a sinusoidal disturbance signal sampled at 0.005 s steps and I need to find the best Kp and Kd in the range [1.0, 50.0] for Kp and [0.05, 3.0] for Kd, using 8 grid points per axis, starting from baseline Kp=10.0 and Kd=0.5, while keeping peak error below 0.3 and RMS error below 0.05.”
Mobile robot motor gain optimization
“My mobile robot's drive motor model has mass 0.8 kg and damping 0.3. I captured a disturbance trace of 512 samples at 0.01 s timestep and want you to grid-search 6 points per axis over Kp from 2.0 to 30.0 and Kd from 0.0 to 2.0, with baseline Kp=8.0 and Kd=0.4, and find gains satisfying a maximum peak error of 1.0 and maximum RMS error of 0.2.”
Industrial conveyor PD gain sweep
“I need to tune a conveyor belt controller — the plant has mass 50.0 kg and damping 5.0. My disturbance profile has 200 samples at a 0.02 s time step. Please search 12 grid points per axis over Kp in [5.0, 200.0] and Kd in [0.5, 10.0] starting from Kp=40.0 and Kd=2.0, with maximum allowable peak error of 2.0 and RMS error of 0.5.”
Technical details
Endpoint
POSThttps://jobs.rqmtechnologies.com/x402/buyer-jobs/robotics.optimize-disturbance-rejection.v1When to prefer
Use this endpoint when you have a parameterized mass-damper plant model with a known bounded disturbance signal and explicit numerical objectives (peak and RMS error limits), and you want to find optimal PD gains via grid search without deploying to hardware. Prefer this over manual tuning or generic optimization libraries when you need a reproducible, cloud-executed sweep with explicit constraint checking at $0.01 per call.
Side effects
- Disturbance array below minimum length (< 2 samples) or above 4096 samples
- mass must be strictly positive; zero or negative mass returns validation error
- grid_points_per_axis outside [2, 32] range causes schema rejection
- No gain combination in the search grid satisfies the peak or RMS constraints — returns failure with best achieved metrics
- time_step_seconds must be strictly positive and ≤ 10
- Kp or Kd min/max bounds are inverted (min > max) causing invalid search space
- max_total_price too low — payment authorization fails before computation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| request | object | |
| schema_version | — | |
| idempotency_key | string | |
| max_total_price | string |
Tags
x402
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