Best use case
motor-control is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Motor control algorithms and driver implementation
Teams using motor-control should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/motor-control/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How motor-control Compares
| Feature / Agent | motor-control | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Motor control algorithms and driver implementation
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Motor Control Skill
## Overview
This skill provides motor control algorithm implementation and driver development expertise for embedded systems controlling DC, BLDC, stepper, and AC induction motors.
## Capabilities
### PWM Generation
- Center-aligned PWM configuration
- Dead-time insertion
- Complementary output setup
- PWM frequency selection
- Duty cycle modulation
- SVPWM (Space Vector PWM)
### Control Algorithms
- FOC (Field-Oriented Control)
- Scalar V/f control
- Six-step commutation
- Sinusoidal commutation
- DTC (Direct Torque Control)
- Model predictive control
### Position/Speed Feedback
- Encoder interface (quadrature)
- Hall sensor configuration
- Resolver interface
- Sensorless algorithms
- Back-EMF zero-crossing
- Observer-based estimation
### Current Sensing
- Shunt resistor configuration
- Current amplifier setup
- ADC synchronization with PWM
- Oversampling strategies
- DC offset compensation
- Phase reconstruction
### Control Loops
- Current loop (torque control)
- Speed loop (velocity control)
- Position loop (servo control)
- Anti-windup strategies
- Feed-forward compensation
- Gain tuning methods
### Motor Identification
- Parameter measurement
- Auto-tuning procedures
- Resistance/inductance measurement
- Back-EMF constant
- Inertia estimation
### Protection Features
- Overcurrent protection
- Overvoltage protection
- Overtemperature monitoring
- Stall detection
- Safe torque off (STO)
## Target Processes
- `device-driver-development.js` - Motor driver implementation
- `real-time-architecture-design.js` - Real-time control design
- `isr-design.js` - Control loop ISR design
## Dependencies
- Motor control libraries (ST MC SDK, TI MotorWare)
- DSP libraries for fixed-point math
- Encoder/Hall sensor hardware
## Usage Context
This skill is invoked when tasks require:
- Motor driver development
- FOC algorithm implementation
- Position/speed control
- Motor parameter tuning
- Protection circuit design
## Motor Types Supported
| Type | Control Method | Feedback |
|------|---------------|----------|
| Brushed DC | PWM duty cycle | Encoder optional |
| BLDC | Six-step, FOC | Hall, encoder, sensorless |
| PMSM | FOC | Encoder, resolver, sensorless |
| Stepper | Step/direction, microstepping | Open-loop, encoder |
| AC Induction | V/f, FOC | Encoder, sensorless |
## FOC Implementation Example
```c
typedef struct {
float i_alpha, i_beta; // Clarke transform output
float i_d, i_q; // Park transform output
float v_d, v_q; // Voltage commands
float v_alpha, v_beta; // Inverse Park output
float theta; // Rotor angle
float speed; // Rotor speed
} foc_state_t;
void foc_current_loop(foc_state_t* state, float i_a, float i_b, float i_c) {
// Clarke transform
clarke_transform(i_a, i_b, i_c, &state->i_alpha, &state->i_beta);
// Park transform
park_transform(state->i_alpha, state->i_beta, state->theta,
&state->i_d, &state->i_q);
// PI controllers
state->v_d = pi_controller(&pid_d, state->i_d_ref - state->i_d);
state->v_q = pi_controller(&pid_q, state->i_q_ref - state->i_q);
// Inverse Park
inv_park_transform(state->v_d, state->v_q, state->theta,
&state->v_alpha, &state->v_beta);
// SVPWM
svpwm_generate(state->v_alpha, state->v_beta, pwm_duties);
}
```
## Configuration
```yaml
motor_control:
motor_type: bldc | pmsm | stepper | induction
control_method: foc | six_step | vf | step_dir
pwm_frequency: 20000 # Hz
current_loop_rate: 20000 # Hz
speed_loop_rate: 1000 # Hz
feedback: encoder | hall | sensorless
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