Sure, let’s unpack that. Chaotic systems blow up tiny errors, so if your model is even a hair off, the control law you design starts to feel like a blindfolded juggler. The trick is to treat the model as a rough sketch, not a blueprint. Use state estimation—think of it as a pair of eyes on the system—to keep the real state in check, and then apply a robust or adaptive controller that can tolerate the model drift. It’s like having a safety net that stretches as the knives shift. The paradox isn’t that we can’t control chaos, it’s that we must accept that our model is a moving target and build the control strategy around that fact, not around perfection.