Many articles have appeared on this site regarding robots that mimic some natural entity. These include robots that are modeled after insects, worms, and tuna.
Since many of these naturally occurring forms have adapted to particular situations over time, this seems like a good way to go. While these systems adapt the shape and movement of natural systems, there is another area in which reference to the natural world is of use. That area is control systems.
Control systems have evolved over time from fixed value controllers to adaptive control systems. A recent article in the Proceedings of the IEEE December 2012, entitled "Cognitive Control" by Haykin, Fatemi, Setoodeh, and Xue, details the methods of cognitive control and gives examples. As with many of the systems modeled on the natural world, they were studied first in areas other than engineering.
For cognitive control that includes neuroscience and psychology, control systems have evolved from open-loop and PID controllers to more adaptive systems. One of the drivers, of course, is the availability of the computing power to realize these systems. Early control systems were implemented in analog devices. I have seen, as a previous generation, spacecraft and simulator controllers that were based on analog technology. While they could be fast, they were not adaptable or easily changed. These weaknesses helped in the push to digital control systems.
As the technology has progressed, providing more computing power and memory, the ability to implement, in a very cost effective way, adaptive and other sophisticated control approaches is now feasible. Adaptive control lets the systems parameters evolve over time, based on actual interaction with the real world. In 1995, I had a car that had an adaptive shifter mechanism. It adjusted the shift points over time based on the driver's style of driving. I never did figure out how to reset it.
With the development of microcontrollers that have extensive processing power and memory, one of the primary requirements of cognitive control can be realized. That is memory. While adaptive control adjusts parameters over time, cognitive control uses memory to implement a reinforcement-learning approach to adapting.
Many microcontrollers today have the memory capabilities to implement this along with signal processing functions implemented in hardware to efficiently implement the learning algorithms. Thus, implementing learning as a part of the control loop is feasible. This allows us to close the information gap, as the article calls it, in a low-power, low-cost controller. This type of capability is becoming possible in areas like the automotive industry yielding much more efficient engines primarily by providing a more sophisticated engine management system.
williamweaver, that is an interesting point about teaching physics. When I was studying physics one thing that really struck me and my classmates was that, if you weren't well prepared for the test, if you knew the basic laws, you could derive anything. Of course, in a test you only have so much time, so it pays to study.
Ann, one can never be sure, but MCUs are increasing in power as well. The latest trend is to combine MCUs with technologies like FPGAs. This increases their power tremendously by combining the logic processing capability of the MCU with the signal processing capability of the FPGA. The MCUs themselves often have some level of signal processing capability built in as with the ARM M3/4 line. Look for a blog on this topic from me soon.
Thanks for the link, Lou. It's fun to learn more about control systems for the robots I've written about: the tuna, worms and bugs you mention. But robots are getting really sophisticated, and I wonder how long MCUs will be able to keep up.
Chuck, I share your amazement in our cognitive abilities. We have seen so many examples of our technology extending our ability to create even more advanced technology and that reinforces my optimism. Thomas Edison famously did not select Tungsten as the filament material for his light bulb because we did not have the material processing technology needed to turn this extremely hard refractory metal into a thin filament. The Human Genome project was projected to take 15 years to complete, but due to innovation along the way provided a rough draft in 10 years (exponential yet again).
When I teach physics, we need to review the basics of time, position, motion, force, work, and energy, but it short order are able to have productive discussions of the Large Hadron Collider and the search for the Higgs Boson. Things are definitely arriving at a rapid pace, but thankfully, our mental models are improving right along with them. =]
Yes, indeed, I believe we are at a nearly-vertical part of the exponential curve right now, Bill. It's frightening to think of what that will mean for the next century. I don't think we have the cognitive abilities to even imagine that.
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