News Column

Findings on Robotics Reported by Investigators at University of Alberta

February 10, 2014



By a News Reporter-Staff News Editor at Robotics & Machine Learning -- Investigators discuss new findings in Robotics. According to news reporting originating in Edmonton, Canada, by VerticalNews journalists, research stated, "It has become standard in the helicopter UAV control literature to use the main and tail rotor thrusts, and the main rotor flapping angles as inputs. However, the physically-controllable inputs are servomotors which actuate the main rotor cyclic and collective pitch, and the tail rotor collective pitch."

The news reporters obtained a quote from the research from the University of Alberta, "Precise treatments of the helicopter model exist which study the physical inputs. However, these models remain intractable for practical implementation motivating researchers to use rough approximations such as simple gain relationships between thrust and collective. We propose and identify a physical input model which retains the accuracy of a general model but is algebraically simple enough for its use in control design."

According to the news reporters, the research concluded: "As a result of experimental validation, the vehicle's velocity is incorporated into the model to improve its accuracy."

For more information on this research see: Control-Oriented Physical Input Modelling for a Helicopter UAV. Journal of Intelligent & Robotic Systems, 2014;73(1-4):209-217. Journal of Intelligent & Robotic Systems can be contacted at: Springer, Van Godewijckstraat 30, 3311 Gz Dordrecht, Netherlands. (Springer - www.springer.com; Journal of Intelligent & Robotic Systems - www.springerlink.com/content/0921-0296/)

Our news correspondents report that additional information may be obtained by contacting B. Godbolt, University of Alberta, Dept. of Elect & Comp Engn, Appl Nonlinear Controls Lab, Edmonton, AB T6G 2V4, Canada.

Keywords for this news article include: Canada, Alberta, Edmonton, Robotics, North and Central America

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Source: Robotics & Machine Learning


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