Nav: Home

Fractional disturbance observers could help machines stay on track

December 22, 2016

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 tools called disturbance observers that make on-the-fly calculations to put a disturbed system back on track.

Disturbance observers are not new to the world of automation. For decades, these algorithms have played an important role in controlling railways, robots, and hard drives. That's because, unlike other algorithms that aim to minimize interference, disturbance observers rely only on the signals that go into and come out of a system; they know nothing about the interfering signal itself.

What is new is how automation algorithms have begun to perceive the world around us. Engineering processes previously described using Newtonian physics and calculus are being recast in the light of so-called fractional calculus. This more general form of calculus is better equipped to model the real processes that affect how automated systems operate, such as battery discharge and the memory-like behavior of electrical circuits.

Using fractional calculus, the team of researchers created a suite of observers that could accurately estimate disturbances of varying complexity. When tested on a model of a gas turbine, two observers clearly outperformed the rest. And when combined, the pair operated well under the harshest conditions, keeping close track of highly fluctuating disturbance signals.

Disturbance monitoring, however, is only half the battle. Once the signal associated with a disturbance is carefully measured, it has to be eliminated. Future studies will be dedicated to figuring out how disturbance observers can be coupled with other control elements to make machines operate even more smoothly.
-end-
Fulltext of the paper is available: http://html.rhhz.net/ieee-jas/html/20160412.htm

Video summary:

http://players.brightcove.net/4887491952001/default_default/index.html?videoId=5211304399001
https://youtu.be/iNuyigyidR8
https://vimeo.com/191820640
http://v.youku.com/v_show/id_XMTgyMjE3NTkyMA==.html

IEEE/CAA Journal of Automatica Sinica (JAS) is a joint publication of the Institute of Electrical and Electronics Engineers, Inc (IEEE) and the Chinese Association of Automation. JAS publishes papers on original theoretical and experimental research and development in all areas of automation. The coverage of JAS includes but is not limited to: Automatic control/Artificial intelligence and intelligent control/Systems theory and engineering/Pattern recognition and intelligent systems/Automation engineering and applications/Information processing and information systems/Network based automation/Robotics/Computer-aided technologies for automation systems/Sensing and measurement/Navigation, guidance, and control.

To learn more about JAS, please visit: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6570654

http://www.ieee-jas.org

Chinese Association of Automation

Related Algorithms Articles:

New algorithm can distinguish cyberbullies from normal Twitter users with 90% accuracy
A team of researchers, including faculty at Binghamton University, have developed machine learning algorithms which can successfully identify bullies and aggressors on Twitter with 90 percent accuracy.
AI learns complex gene-disease patterns
A deep learning model improves the ability to identify genes potentially involved in disease.
New brain map could improve AI algorithms for machine vision
An international team of researchers led by neuroscientists from CSHL and University of Sydney published an updated view on the primate brain's visual system organization, and they found that parts of the primate visual system may work differently than previously thought.
Two new algorithms can identify patients at risk of HIV
Two new studies developed algorithms that can identify patients who are at risk of acquiring HIV and may benefit from preventive care.
Scientists stack algorithms to improve predictions of yield-boosting crop traits
To help researchers better predict high-yielding crop traits, a team from the University of Illinois have stacked together six high-powered, machine learning algorithms that are used to interpret hyperspectral data -- and they demonstrated that this technique improved the predictive power of a recent study by up to 15 percent, compared to using just one algorithm.
Intelligent algorithms for genome research
In order to find out which genes are responsible for diseases such as cancer or diabetes, scientists nowadays frequently resort to using machine-learning models.
Algorithms predicting gene interactions could make cancer treatments more effective
Researchers have developed a database which uses algorithms to predict gene interactions and can help clinicians search for more effective therapeutic solutions to cancer.
SwRI engineers develop novel techniques to trick object detection systems
New adversarial techniques developed by engineers at Southwest Research Institute can make objects 'invisible' to image detection systems that use deep-learning algorithms.
Machine learning methods in precision medicine targeting epigenetics diseases
The huge amounts of epigenetic data coming from biological experiments and clinic, machine learning can help in detecting epigenetic features in genome, finding correlations between phenotypes and modifications in histone or genes, accelerating the screen of lead compounds targeting epigenetics diseases and many other aspects around the study on epigenetics, which consequently realizes the hope of precision medicine.
Slicing optical beams: Cryptographic algorithms for quantum networks
The mathematical models can be used not only for quantum networks and authentication but also for full-scale quantum computing.
More Algorithms News and Algorithms Current Events

Best Science Podcasts 2019

We have hand picked the best science podcasts for 2019. Sit back and enjoy new science podcasts updated daily from your favorite science news services and scientists.
Now Playing: TED Radio Hour

Rethinking Anger
Anger is universal and complex: it can be quiet, festering, justified, vengeful, and destructive. This hour, TED speakers explore the many sides of anger, why we need it, and who's allowed to feel it. Guests include psychologists Ryan Martin and Russell Kolts, writer Soraya Chemaly, former talk radio host Lisa Fritsch, and business professor Dan Moshavi.
Now Playing: Science for the People

#538 Nobels and Astrophysics
This week we start with this year's physics Nobel Prize awarded to Jim Peebles, Michel Mayor, and Didier Queloz and finish with a discussion of the Nobel Prizes as a way to award and highlight important science. Are they still relevant? When science breakthroughs are built on the backs of hundreds -- and sometimes thousands -- of people's hard work, how do you pick just three to highlight? Join host Rachelle Saunders and astrophysicist, author, and science communicator Ethan Siegel for their chat about astrophysics and Nobel Prizes.