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:

python

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#

python

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:

python

def run_test_with_safety(system, input_value, duration, dt, max_limit):
    """Run open-loop test while monitoring safety limits."""
    data = []
    current_input = input_value

    for step in range(int(duration / dt)):
        result = system.step(current_input)
        data.append(result)

        # Safety check
        if result["output"] >= max_limit:
            current_input = 0.0  # Cut input

    return data

Logging Safety Events#

Always log safety events for analysis:

python

safety_log = {
    "limit": max_limit,
    "events": []
}

if measurement >= max_limit:
    safety_log["events"].append({
        "time": current_time,
        "measurement": measurement,
        "command_before": command,
        "command_after": 0.0,
        "event_type": "limit_exceeded"
    })

Pre-Control Checklist#

Before starting any control operation:

1. Verify sensor reading is reasonable
   • Not NaN or infinite
   • Within physical bounds
2. Check initial conditions
   • Measurement should be at expected starting point
   • Output should start at safe value
3. Confirm safety limits are configured
   • Maximum limit threshold set
   • Output clamping enabled

python

def pre_control_checks(measurement, config):
    """Run pre-control safety verification."""
    assert not np.isnan(measurement), "Measurement is NaN"
    assert config.get("max_limit") is not None, "Safety limit not configured"
    return True

Best Practices#

1. Defense in depth: Multiple layers of protection
2. Fail safe: When in doubt, reduce output
3. Log everything: Record all safety events
4. Never bypass: Safety code should not be conditionally disabled
5. Test safety: Verify interlocks work before normal operation