SKILL.md
Safety Interlocks for Control Systems
Overview
Safety interlocks are protective mechanisms that prevent equipment damage and ensure safe operation. In control systems, the primary risks are output saturation and exceeding safe operating limits.
Implementation Pattern
Always check safety conditions BEFORE applying control outputs:
def apply_safety_limits(measurement, command, max_limit, min_limit, max_output, min_output):
"""
Apply safety checks and return safe command.
Args:
measurement: Current sensor reading
command: Requested control output
max_limit: Maximum safe measurement value
min_limit: Minimum safe measurement value
max_output: Maximum output command
min_output: Minimum output command
Returns:
tuple: (safe_command, safety_triggered)
"""
safety_triggered = False
# Check for over-limit - HIGHEST PRIORITY
if measurement >= max_limit:
command = min_output # Emergency cutoff
safety_triggered = True
# Clamp output to valid range
command = max(min_output, min(max_output, command))
return command, safety_triggered
Integration with Control Loop
class SafeController:
def __init__(self, controller, max_limit, min_output=0.0, max_output=100.0):
self.controller = controller
self.max_limit = max_limit
self.min_output = min_output
self.max_output = max_output
self.safety_events = []
def compute(self, measurement, dt):
"""Compute safe control output."""
# Check safety FIRST
if measurement >= self.max_limit:
self.safety_events.append({
"measurement": measurement,
"action": "emergency_cutoff"
})
return self.min_output
# Normal control
output = self.controller.compute(measurement, dt)
# Clamp to valid range
return max(self.min_output, min(self.max_output, output))
Safety During Open-Loop Testing
During calibration/excitation, safety is especially important because there's no feedback control:
