Multi-function air data probes using neural network for...

Data processing: vehicles – navigation – and relative location – Vehicle control – guidance – operation – or indication – Aeronautical vehicle

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C701S014000, C073S182000

Reexamination Certificate

active

06604029

ABSTRACT:

BACKGROUND OF THE INVENTION
The present invention relates to air data systems that provide accurate compensation of sideslip of an air vehicle utilizing independent probes that are not pneumatically coupled, but which have processors for interchanging electrical signals between the probes. These probes are sometimes referred to as multi-function probes (MFPs). One type of MFP is the SmartProbe™ sold by B.F. Goodrich Company. Multi-function probes include processing circuitry located at the probe itself as part of its instrument package. During sideslip of the air vehicle, compensation of various local (to the probes) parameters or signals, such as angle of attack and static pressure, is necessary for accurate determination of aircraft angle of attack and other aircraft parameters including determination of altitude from static pressure or other means. This requirement for accuracy in altitude indications is particularly important in Reduced Vertical Separation Minimum (RVSM) space areas of the air traffic control system.
In conventional air data systems, probes on opposite sides of an aircraft can be pneumatically connected so that the pressure signals are averaged between the right side of the aircraft and the left side of the aircraft to provide a static pressure that is “nearly true”. In most conventional systems, although corrections are made for Mach number and aircraft angle of attack, it is rare that neglecting sideslip effect will introduce enough error to warrant a correction based on sideslip for the cross coupled probes.
However, MFPs are connected only electrically in order to eliminate the need for pneumatic tubing passing between the opposite sides of the aircraft or between probes on the same side of the aircraft. This means that each probe is pneumatically independent, even if it is electrically communicating with other probes. In the RVSM space, there is a need for dual redundant systems for static pressure estimation. While information can easily be exchanged between the processing circuitry of different probes, the need for determining sideslip effect remains. Computational fluid dynamic analysis has shown that position errors can be up to 600 feet per degree of sideslip under typical RVSM space flight conditions at, for example, 41,000 feet and a Mach number of 0.8. It is thus apparent that the sideslip effect must be corrected to obtain the necessary accuracy for certification by aviation authorities.
While the need exists for providing redundant systems for static pressure estimation in the RVSM space, it is also desirable to reduce the number of probes on the exterior of the aircraft. Typically, redundancy is provided using four probes, with two probes positioned on each side of the aircraft. Elimination of one or more of these probes potentially reduces the redundancy available in the system. Reducing the number of probes while maintaining the desired redundancy thus presents a problem.
SUMMARY OF THE INVENTION
The present invention relates to multi-function air data sensing systems which provide for redundancy in correcting for sideslip of an aircraft arriving at various air data parameters, such as aircraft angle of attack, static pressure or pressure altitude, and Mach number. Aerodynamic sideslip is a measure of the magnitude of a cross component of airspeed to the forward component of airspeed. Compensation information exchanged between probes such as MFPs, for example differential and local angle of attack between the two sides of an aircraft, can provide an indication of sideslip effect. Using values of local angle of attack provides information that corresponds to aircraft parameters or variables of angle of attack and angle of sideslip. In accordance with embodiments of the invention disclosed herein, a neural network is used to provide sideslip compensated air data parameters, using as inputs both pressure information sensed by the corresponding air data probe and inertial yaw angle or angle rate information provided by an inertial navigation system. Using the inertial information and a trained neural network, redundancy in air data parameter calculation can be provided, thus potentially reducing the number of probes.
An air data sensing probe or MFP of the invention includes a barrel having multiple pressure sensing ports for sensing multiple pressures. Instrumentation coupled to the pressure sensing ports provides electrical signals indicative of the pressures. An inertial navigation system input of the probe receives electrical signals indicative of inertial navigation data for the aircraft. A neural network of the probe receives as inputs the electrical signals indicative of the multiple pressures and the electrical signals indicative of the inertial navigation data. The neural network is trained or configured to provide as an output electrical signals indicative of an air data parameter compensated for sideslip conditions.


REFERENCES:
patent: 3318146 (1967-05-01), DeLeo et al.
patent: 4096744 (1978-06-01), DeLeo et al.
patent: 4303978 (1981-12-01), Shaw et al.
patent: 4378696 (1983-04-01), DeLeo et al.
patent: 4378697 (1983-04-01), DeLeo et al.
patent: 5205169 (1993-04-01), Hagen
patent: 5319970 (1994-06-01), Peterson et al.
patent: 5423209 (1995-06-01), Nakaya et al.
patent: 5485412 (1996-01-01), Sarkkinen et al.
patent: 5610845 (1997-03-01), Slabinski et al.
patent: 5797105 (1998-08-01), Nakaya et al.
patent: 5901272 (1999-05-01), Schaefer, Jr. et al.
patent: 6253166 (2001-06-01), Whitmore et al.
patent: 44 10 709 (1995-10-01), None
patent: WO 99/32963 (1999-01-01), None
“BFGoodrich—Aircraft Sensors Division Air Data System with SmartProbe for Fairchiled Dornier 728JET”, BFGoodrich—Rosemount Aerospace, Addendum to D9820217 Rev. B, Oct. 1998, pp. 1-10.
“SmartProbe™ Air Data System for Embraer ERJ-170 & 190”, BFGoodrich—Aircraft Sensors Division, Proposal D9920133, Apr. 1999, pp. 1-65.
F.W. Hagen and Dr. H. Seidel, “Deutsche Airbus Flight Test of Rosemount Smart Probe for Distributed Air Data System”, IEEE AES Systems Magazine, Apr. 1994, pp 7-14.
Bulletin 1013, “Pitot and Pitot-Static Probes”, BFGoodrich (May 1998).
T.J. Rohloff, S.A. Whitmore and I. Catton, “Air Data Sensing from Surface Pressure Measurements Using a Neural Network Method”, AIAA Journal, vol. 36, No. 11, Nov. 1998, pp. 2095-2101.
T.J. Rohloff, S.A. Whitmore and I. Catton, “Fault-Tolerant Neural Network Algorithm for Flush Air Data Sensing”, Journal of Aircraft, vol. 36, No. 3, May-Jun. 1999, pp. 541-549.
T.J. Rohloff and I. Catton, “Fault Tolerance and Extrapolation Stability of a Neural Network Air-Data Estimator”, Journal of Aircraft, vol. 36, No. 3, May-Jun. 1999, pp. 571-576.
“What is an Aircraft Neural Network?”, Battelle Memorial Institute (Copyright 1997), (Publication at least by Apr. 9, 2001), http://www.emsl.pnl.gov:2080/proj
euron
eural/what.html.
C. Stergiou, “What is a Neural Network?”, (Publication at least by Apr. 9, 2001), http://www.doc.ic.ac.uk/~nd/surprise_96/journal/voll/csll/articles1.html.
D. Clark, “An Introduction to Neural Networks”, Copyright 1991, 1997) (Publication at least by Apr. 9, 2001), http://members.home.net
euralnet/introtonn/index.htm.

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Multi-function air data probes using neural network for... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Multi-function air data probes using neural network for..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Multi-function air data probes using neural network for... will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-3101259

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.