Fractional disturbance observers could help machines stay on track

Roads are paved with obstacles than can interfere with our driving. They can be as easy to avoid or adjust to as far-away debris or as hard to anticipate as strong gusts of wind. As self-driving cars and other autonomous vehicles become a reality, how can researchers make sure these systems remain in control under highly uncertain conditions? A team of automation experts may have found a way. Using a branch of mathematics called fractional calculus, the researchers created algorithmic disturbance observers that make on-the-fly calculations to put a disturbed system back on track.

Read the whole article on Physorg

More from Physorg