A sophisticated PID control system simulator for motor control applications, featuring real-time visualization and comprehensive parameter tuning capabilities.
PIDlab 7 is a Python-based graphical application designed for engineers, students, and robotics enthusiasts to simulate and analyze PID (Proportional-Integral-Derivative) control systems. The simulator provides accurate motor modeling with support for various motor types commonly used in robotics applications.
- Pre-configured Motors: NEO, Kraken, and tiny brush motors with accurate parameters
- Custom Motor Configuration: Define your own motor parameters including inertia, resistance, torque constants, and current limits
- Gearbox Integration: Configurable gear ratios for mechanical advantage calculations
- Speed Control: PID control for angular velocity regulation
- Position Control: PID control for precise angular positioning
- Feedforward Control: Velocity feedforward (kF) for improved tracking performance
- Free PID Mode: Basic PID control without external loads
- Vertical Load-Bearing PIDF Mode: Advanced mode with gravity compensation and load inertia modeling for applications like elevators or arm mechanisms
- SI Units: Standard international units (rad/s, rad, kg, m, Nm)
- Imperial Units: US customary units (deg/s, deg, lb, ft, lb⋅ft)
- Current Limiting: Realistic motor current limiting based on back-EMF and resistance
- Motor Inversion: Support for inverted motor configurations
- Multiple Target Sequences: Define complex motion profiles with multiple setpoints over time
- Acceptable Error Bands: Visual indication of when the system reaches acceptable performance
- Real-time Visualization: Three synchronized plots showing output response, control voltage, and motor current
- Python 3.7+
- tkinter (usually included with Python)
- matplotlib
- numpy
- Clone or download the repository
- Install required packages:
pip install matplotlib numpy
- Run the application:
python pidlab7.py
- Select Motor Type: Choose from preset motors or configure custom parameters
- Set PID Parameters: Tune kp, ki, and kd values for desired performance
- Configure Targets: Set target values and their activation times
- Choose Control Mode: Select speed or position control
- Run Simulation: The system automatically updates as you modify parameters
- Gearbox: Enter gear ratio as motor rotations per output rotation
- Load Modeling: In PIDF mode, specify mass and radius for gravity compensation
- Current Limits: Set maximum motor current for realistic simulation
- Acceptable Error: Define tolerance bands for performance evaluation
- Top Plot: System response (speed or position) vs. time with target lines and error bands
- Middle Plot: Control voltage output showing PID effort
- Bottom Plot: Motor current consumption for power analysis
| Motor | Inertia (kg⋅m²) | Resistance (Ω) | Kt (Nm/A) | Ke (V⋅s/rad) |
|---|---|---|---|---|
| NEO | 1.5×10⁻⁶ | 0.02 | 0.027 | 0.027 |
| Kraken | 6×10⁻⁶ | 0.015 | 0.035 | 0.035 |
| Tiny Brush | 5×10⁻⁷ | 2.0 | 0.03 | 0.03 |
- Robotics Education: Learn PID control principles with realistic motor models
- System Design: Prototype and validate control systems before hardware implementation
- Parameter Tuning: Optimize PID gains for specific motor and load combinations
- Performance Analysis: Evaluate system response, power consumption, and stability
MIT License - see LICENSE.txt for details.
Copyright (c) 2025 Team Resistance
The simulator uses accurate motor physics including:
- Electrical time constants based on L/R ratios
- Mechanical damping effects
- Back-EMF compensation
- Current limiting with voltage saturation
- Gravity compensation for vertical load applications
- Semi-implicit Euler integration for numerical stability
For questions or contributions, please refer to the project repository.