Aircraft design cycle time reduction using artificial intelligence

In this work we show how Artificial Intelligence (AI) could effectively be used to expedite the decision making process in the early stages of the aircraft design process. We employ both Fuzzy Logic (FL) and Neural Network (NN) as two different schemes of the AI. The developed tools are intended to help to select the proper combination of engine thrust, wing area and the aircraft weight without going through elaborate details of other direct approaches. We further show how the AI could be applied to the specific class of light business jets which serves to validate these schemes. The results indicate the effectiveness of the AI approach in the preliminary aircraft design process. The actual and approximated values for the take-off wing loading and the take-off thrust loading are in agreement within ten percent. The developed design tools, therefore, prove to be effective to decrease aircraft design cycle time.

[1]  Ali Reza Babaei,et al.  Classical and fuzzy-genetic autopilot design for unmanned aerial vehicles , 2011, Appl. Soft Comput..

[2]  Ching-Chih Tsai,et al.  Nonlinear adaptive aggressive control using recurrent neural networks for a small scale helicopter , 2010 .

[3]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[4]  Juan J. Alonso,et al.  Aircraft design optimization , 2009, Math. Comput. Simul..

[5]  Charles S. Wasson,et al.  System Analysis, Design, and Development , 2008 .

[6]  F. H. Nordin,et al.  Fuzzy bang-bang relay controller for satellite attitude control system , 2010, Fuzzy Sets Syst..

[7]  Zhang Hua-min,et al.  Neutral-Network -Based Output-Redefinition Control of an Unmanned Aerial Vehicle , 2011 .

[8]  J. H. Kim,et al.  Multidisciplinary aircraft design and evaluation software integrating CAD, analysis, database, and optimization , 2006, Adv. Eng. Softw..

[9]  Min-Jea Tahk,et al.  Neural network guidance based on pursuit-evasion games with enhanced performance , 2002 .

[10]  An-Min Zou,et al.  Adaptive attitude control of spacecraft without velocity measurements using Chebyshev neural network , 2010 .

[11]  Oktay Baysal,et al.  Vibrational genetic algorithm enhanced with fuzzy logic and neural networks , 2010 .

[12]  Wen Liu,et al.  Minimizing interference in satellite communications using transiently chaotic neural networks , 2009, Comput. Math. Appl..

[13]  M. A. Shahi Ashtiani,et al.  Optimum Selection of Number of Seats/Cargo Volume for Transports in Uncertain Business Environment , 2008 .

[14]  Luis F. Gonzalez,et al.  Robust evolutionary algorithms for UAV/UCAV aerodynamic andRCS design optimisation , 2008 .

[15]  Dae-Woo Lee,et al.  Control of approach and landing phase for reentry vehicle using fuzzy logic , 2011 .

[16]  Carlos Silvestre,et al.  Embedded UAV model and LASER aiding techniques for inertial navigation systems , 2010 .

[17]  Cong Sun,et al.  Automated implementation of a design principle during the optimization of conceptual aircraft , 2007, Knowl. Based Syst..

[18]  Charles S. Wasson System Analysis, Design, and Development: Concepts, Principles, and Practices (Wiley Series in Systems Engineering and Management) , 2005 .

[19]  Dong-li Ma,et al.  Multidisciplinary Design-Optimization Methods for Aircrafts using Large-Scale System Theory , 2009 .

[20]  Stamatios V. Kartalopoulos,et al.  Understanding neural networks and fuzzy logic - basic concepts and applications , 1997 .

[21]  Richard Curran,et al.  An integrated systems engineering approach to aircraft design , 2006 .

[22]  Tarek Hamel,et al.  Attitude and gyro bias estimation for a VTOL UAV , 2006 .

[23]  Ranjan Ganguli,et al.  A direct adaptive neural command controller design for an unstable helicopter , 2005, Eng. Appl. Artif. Intell..

[24]  Antony Jameson,et al.  50 years of transonic aircraft design , 2011 .

[25]  An-Min Zou,et al.  Adaptive fuzzy fault-tolerant attitude control of spacecraft , 2011 .

[26]  Aydogan Savran,et al.  Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks. , 2006, ISA transactions.

[27]  Qi Li,et al.  Adaptive fuzzy PID composite control with hysteresis-band switching for line of sight stabilization servo system , 2011 .

[28]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .

[29]  Peter Horst,et al.  Structural sizing for an unconventional, environment-friendly aircraft configuration within integrated conceptual design☆ , 2008 .

[30]  Martin T. Hagan,et al.  Neural network design , 1995 .

[31]  Robert Babuska,et al.  Design of a gain-scheduling mechanism for flight control laws by fuzzy clustering , 2006 .

[32]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[33]  Pierre Apkarian,et al.  Lateral flight control design for a highly flexible aircraft using nonsmooth optimization , 2011 .

[34]  Jong-Hun Kim,et al.  Development of an electro-optical system for small UAV , 2010 .

[35]  Yong Zhang,et al.  Research on computer vision-based for UAV autonomous landing on a ship , 2009, Pattern Recognit. Lett..

[36]  Daniel P. Raymer,et al.  Aircraft Design: A Conceptual Approach , 1989 .

[37]  Jae-Woo Lee,et al.  Comprehensive aircraft configuration design tool for Integrated Product and Process Development , 2011, Adv. Eng. Softw..

[38]  Chin-Hsing Cheng,et al.  Application of GA-based neural network for attitude control of a satellite , 2010 .

[39]  Wei Zheng,et al.  Variable Structure Attitude Control for an UAV with Parameter Uncertainty and External Disturbance , 2011 .

[40]  Yu-Ping Lin,et al.  A fuzzy guidance law for vertical launch interceptors , 2009 .

[41]  Sergio de La Parra,et al.  Low cost navigation system for UAV's , 2005 .

[42]  Xiongqing Yu,et al.  Aerodynamic/Stealthy/Structural Multidisciplinary Design Optimization of Unmanned Combat Air Vehicle , 2009 .

[43]  Ajoy Kumar Kundu,et al.  Aircraft Design , 1940, Nature.

[44]  E E Wilson,et al.  Aircraft Engine Design , 1925 .

[45]  Bernard Grossman,et al.  Multidisciplinary design optimization of blended-wing-body transport aircraft with distributed propulsion , 2013 .

[46]  Chin-Hsing Cheng,et al.  Attitude control of a satellite using fuzzy controllers , 2009, Expert Syst. Appl..

[47]  Enrico Cestino,et al.  Design of solar high altitude long endurance aircraft for multi payload & operations , 2006 .

[48]  Hyochoong Bang,et al.  Adaptive attitude control of spacecraft using neural networks , 2009 .

[49]  Antonios Tsourdos,et al.  Fuzzy multi-objective design for a lateral missile autopilot , 2006 .

[50]  Abdulrahman H. Bajodah,et al.  Real time adaptive nonlinear model inversion control of a twin rotor MIMO system using neural networks , 2012, Eng. Appl. Artif. Intell..

[51]  Sundaram Suresh,et al.  Direct adaptive neural flight control system for an unstable unmanned aircraft , 2008, Appl. Soft Comput..

[52]  Jorg Onno Entzinger,et al.  Modeling of the Visual Approach to Landing Using Neural Networks and Fuzzy Supervisory Control , 2010 .

[53]  Okyay Kaynak,et al.  Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles , 2007, Expert Syst. Appl..

[54]  J. J. Korte,et al.  Multidisciplinary Optimization Methods for Preliminary Design , 1997 .

[55]  Ji-Zhen Liu,et al.  Adaptive fuzzy sliding mode control for flexible satellite , 2005, Eng. Appl. Artif. Intell..

[56]  Hassan Ugail,et al.  Parametric design of aircraft geometry using partial differential equations , 2009, Adv. Eng. Softw..

[57]  Amir Safari,et al.  Tuning of fuzzy fuel controller for aero-engine thrust regulation and safety considerations using genetic algorithm , 2011 .

[58]  Laurence Tianruo Yang,et al.  Fuzzy Logic with Engineering Applications , 1999 .