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RQM Tune Controller To Objectives
x402
RQM Tune Controller To Objectives 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 a proportional-gain controller within caller-supplied gain bounds and safety limits to meet a target error objective.
Price / call$0.01
Success—
StatusUnknown
How this service works
Problem: Tune a supported controller only within supplied bounds and safety limits. Input: JSON with supported controller, current gain, fixture error, target error.... Result: typed verdict, measured metrics, candidate only when verified. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.
Output
Returns a tuned proportional gain value computed to bring fixture error toward the target error, constrained within the provided minimum and maximum gain bounds and below the safety limit. The response is a structured job result conforming to the RQM bazaar buyer job schema.
Example use cases
Robotic arm gain optimization
“I need to tune my robot arm's proportional gain controller. Current gain is 5.0, we're seeing a fixture error of 1.2 and want to get down to a target error of 0.2. Gain must stay between 0.0 and 50.0 with a safety limit of 45.0 — what gain should I use?”
Automated control loop tightening
“My control loop has drifted — current proportional gain is 20.0, current fixture error is 2.5, target is 0.5. Bounds are 0 to 200, safety limit 180. Can you compute the adjusted gain I should apply?”
Pre-deployment controller validation
“Before we run our next test, tune the proportional gain controller: current gain 8.0, fixture error 0.6, target error 0.05, min gain 0.0, max gain 80.0, safety limit 75.0. What gain value satisfies the target within bounds?”
Technical details
Endpoint
POSThttps://jobs.rqmtechnologies.com/x402/buyer-jobs/robotics.tune-controller-to-objectives.v1When to prefer
Use this endpoint when you need a bounded, safe, software-computed proportional gain recommendation for a control loop and you can supply explicit current gain, fixture error, target error, and gain limits. It is ideal for automated robotics pipelines that need repeatable, rule-constrained tuning without physical hardware access or safety certification requirements. Do not use for live hardware control, physical safety certification, or controller types other than proportional_gain.
Side effects
- Invalid or unsupported controller type (only 'proportional_gain' is accepted)
- Gain bounds infeasible (minimum_gain > maximum_gain)
- Safety limit below minimum_gain making tuning impossible
- Target error not achievable within supplied gain bounds
- Schema validation failure if required fields are missing or out of allowed range (0–1,000,000)
- Payment failure or insufficient USDC balance via x402 protocol
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| request | object | |
| schema_version | — | |
| idempotency_key | string | |
| max_total_price | string |
Tags
x402
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