While Davis Technologies’ Profiler wheel speed management system has become a prime tool in the arsenal of drag racers the world over, and as such, has garnered a lot of the headlines. But its self-learning traction control systems, which have been around for years, are still an incredibly powerful technology that racers continue to employ and even use in unison with the Profiler.
The self-learning traction control, as the name implies, uses lightning-quick processors to sense rates of change in driveshaft RPM and make corrections in ignition timing based on parameters set by the user. Self-learning units rely on minor inputs — the user can adjust the sensitivity so the device picks up or ignores sudden rates of change, as well as the rate at which timing will be retarded based on individual rates of change.
The Profiler, in comparison, involves pre-plotting a run in great detail, and while the end result is oft-considered unparalleled, not everyone can plan how a run is going to play out. No-prep racing, for example, is far less predictable than prepped-track racing, and so relying on the traction control to self-adjust going down the racetrack is hugely beneficial. As Shannon Davis notes in this guided walk-through of self-learning traction control, some racers will even use a Profiler through the first two or three gear changes, and then switch to the self-learning system for the remainder of the run — that way they have fine control of part of the run, and the technology can largely manage the rest.
As Davis moves through his walk-through, you’ll see an example of a Profiler curve and a self-learning curve in a simulation of a run, where you can view the differences in how they see and respond to losses of traction and gear changes. Davis notes that even without any plotting, the self-learning systems “do a hell of a job.”