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simulation-metrics

Calculate rise time, overshoot, steady-state error, and settling time from control system response data.

98|12|Updated May 15, 2026
Search Tags:#performance analysis#overshoot#control system#simulation metrics#steady-state error
One-click install
npx skills add https://github.com/agentscope-ai/PawBench --skill simulation-metrics-agentscope-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simulation-metrics
Source: https://github.com/agentscope-ai/PawBench/tree/main/data/pawbench-v1.0/assets/T127_skillsbench_adaptive-cruise-control/skills/simulation-metrics
Command: npx skills add https://github.com/agentscope-ai/PawBench --skill simulation-metrics-agentscope-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This skill provides a tool to accurately measure and evaluate the performance metrics of a control system based on simulation results, helping users determine parameters such as rise time, overshoot percentage, and settling time.

Core Features & Use Cases

  • Performance Metrics Calculation: Calculates and measures performance metrics like rise time, overshoot percentage, steady-state error, and settling time.
  • Application: Ideal for users who require precise performance assessments for their control system simulations.
  • Use Case: If you have simulation results from a control system and want to determine its response performance, this skill can provide the necessary calculations.

Quick Start

To use this skill, provide the time and value arrays of a system response, along with the target value and relevant parameters, such as 'rise_time(times, values, target)'.

Frequently Asked Questions about simulation-metrics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate control system performance metrics from simulation results?▼

You calculate control system performance metrics by providing time and value arrays of a system response, along with the target value, to evaluate parameters like rise time and overshoot. This allows precise assessment of your control system's response performance.

What specific performance metrics are measured for a control system response?▼

Measured performance metrics for a control system response include rise time, overshoot percentage, steady-state error, and settling time. These metrics are calculated based on the system response times and values to determine overall performance.

Can I calculate steady-state error and overshoot percentage using standard Python libraries?▼

You can calculate steady-state error and overshoot percentage using standard Python libraries. The calculation is executed through Python scripts that evaluate your control system simulation data to accurately measure the required performance metrics.

What input data do I need to measure rise time and settling time for my simulations?▼

You need to input time and value arrays representing the system response, along with the target value and relevant parameters, to measure rise time and settling time for your simulations. This data allows the scripts to calculate the required metrics accurately.

Is this approach suitable for evaluating any control system simulation?▼

This approach is suitable for evaluating any control system simulation. If you have simulation results from a control system and want to determine its response performance, these calculations provide the necessary performance assessments accurately.

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