# RQM Tune Controller To Objectives

> 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.

## Facts

- Endpoint: POST https://jobs.rqmtechnologies.com/x402/buyer-jobs/robotics.tune-controller-to-objectives.v1
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/rqm-tune-controller-to-objectives-d87d5883
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Wupq2JQJ57KnKEODMZhKC

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability rqm-tune-controller-to-objectives-d87d5883 -d '<json body>'
```

Example prompt: Tune my proportional gain controller — current gain is 12.5, fixture error is 0.8, target error is 0.1, minimum gain 0.0, maximum gain 100.0, and safety limit 95.0. Give me the recommended tuned gain.

## When to prefer this

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.

## Known failure modes

- 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

## 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.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "request": {
   "type": "object",
   "title": "ControllerTuneRequest",
   "required": [
    "supported_controller",
    "current_gain",
    "fixture_error",
    "target_error",
    "minimum_gain",
    "maximum_gain",
    "safety_limit"
   ],
   "properties": {
    "plant_mass": {
     "type": "number",
     "title": "Plant Mass",
     "default": 1,
     "maximum": 1000000,
     "exclusiveMinimum": 0
    },
    "grid_points": {
     "type": "integer",
     "title": "Grid Points",
     "default": 21,
     "maximum": 101,
     "minimum": 3
    },
    "time_step_s": {
     "type": "number",
     "title": "Time Step S",
     "default": 0.05,
     "maximum": 1,
     "exclusiveMinimum": 0
    },
    "current_gain": {
     "type": "number",
     "title": "Current Gain",
     "maximum": 1000000,
     "minimum": 0
    },
    "maximum_gain": {
     "type": "number",
     "title": "Maximum Gain",
     "maximum": 1000000,
     "minimum": 0
    },
    "minimum_gain": {
     "type": "number",
     "title": "Minimum Gain",
     "maximum": 1000000,
     "minimum": 0
    },
    "safety_limit": {
     "type": "number",
     "title": "Safety Limit",
     "maximum": 1000000,
     "minimum": 0
    },
    "target_error": {
     "type": "number",
     "title": "Target Error",
     "maximum": 1000000,
     "minimum": 0
    },
    "fixture_error": {
     "type": "number",
     "title": "Fixture Error",
     "maximum": 1000000,
     "minimum": 0
    },
    "plant_damping": {
     "type": "number",
     "title": "Plant Damping",
     "default": 0.1,
     "maximum": 1000000,
     "minimum": 0
    },
    "maximum_peak_error": {
     "anyOf": [
      {
       "type": "number",
       "maximum": 1000000000,
       "minimum": 0
      },
      {
       "type": "null"
      }
     ],
     "title": "Maximum Peak Error",
     "default": null
    },
    "holdout_disturbances": {
     "type": "array",
     "items": {
      "type": "number"
     },
     "title": "Holdout Disturbances",
     "maxItems": 4096
    },
    "maximum_peak_control": {
     "anyOf": [
      {
       "type": "number",
       "maximum": 1000000000,
       "minimum": 0
      },
      {
       "type": "null"
      }
     ],
     "title": "Maximum Peak Control",
     "default": null
    },
    "supported_controller": {
     "type": "string",
     "const": "proportional_gain",
     "title": "Supported Controller"
    },
    "training_disturbances": {
     "type": "array",
     "items": {
… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/rqm-tune-controller-to-objectives-d87d5883/health.json
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