No system sees clearly.
Even the most advanced sensor, the most refined model, the most elegant controller—hears the world through noise.
A gust of wind. A jitter in GPS. A flicker in the accelerometer.
Not failure—just imperfection, folded into the very nature of physical systems.
This is what defines a noisy system.
In control theory, noise is not just static—it’s a persistent companion.
It slips into measurements. It clouds estimation. It alters how the system behaves, and how the controller reacts.
There are two places noise enters, and each tells a different story:
1. Process noise—unmodeled dynamics, external disturbances, hidden variables.
It changes the state of the system itself. An aircraft might experience sudden gusts, or unpredictable load changes—forces the controller never sees directly, but must respond to all the same.
2. Measurement noise—sensor errors, latency, resolution limits.
It corrupts the data the controller receives. The aircraft might be perfectly level, but the gyroscope whispers otherwise. The GPS lags. The system reacts not to what is, but to a blurred version of it.
To control in this environment is not to eliminate noise. That’s impossible.
It is to design a system that remains stable, responsive, and intentional—even when it’s being misled.
This is where the architecture of robustness and estimation come into play:
– Kalman filters and extended Kalman filters estimate the true state from noisy measurements, blending prediction and observation into something more trustworthy.
– Input-to-State Stability (ISS) ensures the system stays bounded, even as noise presses in.
– H-infinity control minimizes the worst-case amplification of disturbance, preparing the system for the most adversarial interpretation of noise.
– Stochastic control directly incorporates noise models into optimization—shaping decisions with probability, not just precision.
But above all, the controller must learn one thing: not to overreact.
In a noisy system, the best response is not always the fastest.
It is the wisest—the one that sees past the momentary error, and understands the pattern beneath.
In real-world autonomous flight, this means:
– Ignoring a single GPS glitch in favor of longer-term inertial estimates.
– Trusting trends over spikes.
– Reacting with measured belief, not blind obedience.
A noisy system teaches humility.
It reminds us that knowledge is never complete.
That even the best models are approximations.
That even certainty is filtered through static.
But in that uncertainty, we find a deeper kind of control.
Not perfect, but resilient.
Not sharp, but steady.
Because in the real world, the system that succeeds is not the one that never hears noise—
It’s the one that knows how to listen through it.