As Easy as Riding a Bike

But as those children grow up and learn to ride, they are mastering a very complex process. Mont Hubbard and Ron Hess, each now a professor emeritus of mechanical and aerospace engineering at the University of California, Davis, worked on the project of modeling the system of bicycle and human rider from the point of view of mechanical engineering.

“Everyone knows how to ride a bicycle, but nobody knows how we ride a bicycle,” Professor Hubbard said. “We think our way of imagining the human process is a way to understand that second half.”

This is a system involving a human and a machine, and, he said, “the human is receiving sensory information and there is a control system in the brain creating the right corrective forces that need to be applied to the handlebar to get the bicycle to stand up.”

But processing that information isn’t simple, by any means. Dr. Hess said it turned out to be more complicated than modeling pilots and airplanes.

To make adjustments and keep the bike upright and moving forward, the human brain has to receive sensory information about the position and motion of the body in space and the pressure applied to the handlebars, visual information, and vestibular information about balance and the roll rate of the bike. “All three of those have to be used successfully to ride a bike,” Dr. Hess said, with information coming in from different kinds of sensory receptors, in the eye, in the muscles, in the inner ear.

“If the rider doesn’t do the right thing, if his or her actions aren’t adequate to stabilize the bicycle, then the bicycle will fall over,” Dr. Hubbard said. In engineering, he said, this is called a control law: “The control law says, if I’m falling to the right, what do I do, I turn the wheels to the right,” in a constant process of subtle adjustment and balance to keep the bicycle upright and moving forward. The rider may not understand the differential equations in the model, he said, but the brain is making these calculations.

In their model, “the human takes those streams of information and creates from those various streams a control signal which is a force to push the handle bars,” Dr. Hubbard said. They were able to build a bicycle that could right itself, using these equations to make adjustments, taking into account the important variables like the tilt angle of the bicycle. The model predicted, for example, that before making a turn to one side, there would first be a smaller steering turn to the other side, and Dr. Hess said he had found it satisfying to see that borne out by observing skilled riders.

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