Control Power Optimization using Artificial Intelligence for Forward Swept Wing and Hybrid Wing Body Aircraft

Many futuristic aircraft such as the Hybrid Wing Body have numerous control surfaces that can result in large hinge moments, high actuation power demands, and large actuator forces/moments. Also, there is no unique relationship between control inputs and the aircraft response. Distinct sets of control surface deflections may result in the same aircraft response, but with large differences in actuation power. An Artificial Neural Network and a Genetic Algorithm were used here for the control allocation optimization problem of a Hybrid Wing Body to minimize the Sum of Absolute Values of Hinge Moments for a 2.5G pull-up maneuver. To test the versatility of the same optimization process for different aircraft configurations, the present work also investigates its application on the Forward Swept Wing aircraft. A method to improve the robustness of the process is also presented. Constraints on the load factor and longitudinal pitch rate were added to the optimization to preserve the trim constraints on the control deflections. Another method was developed using stability derivatives. This new method provided better results, and the computational time was reduced by two orders of magnitude. A hybrid scheme combining both methods was also developed to provide a real-time estimate of the optimum control deflection schedules to trim the airplane and minimize the actuation power for changing flight conditions (Mach number, altitude and load factor) in a pull-up maneuver. Finally, the stability derivatives method and the hybrid scheme were applied for an antisymmetric, steady roll maneuver. Control Power Optimization using Artificial Intelligence for Forward Swept Wing and Hybrid Wing Body Aircraft Moustaine Adegbindin GENERAL AUDIENCE ABSTRACT Many futuristic aircraft such as the Hybrid Wing Body have numerous control surfaces that can result in large actuation power. An Artificial Neural Network and a Genetic Algorithm were used here to minimize the actuation power on the Hybrid Wing Body. To test the versatility of the same optimization process for different aircraft configurations, the present work also investigates its application on the Forward Swept Wing aircraft. A method to improve the robustness of the process is also presented. A completely different method was developed, and it provided better results with the computational time reduced by two orders of magnitude. A hybrid scheme combining both methods was also developed to provide a real-time estimate of the optimum control deflection schedules to trim the airplane and minimize the actuation power for changing flight conditions (Mach number, altitude and load factor) in a pull-up maneuver.

[1]  Andy J. Keane,et al.  Recent advances in surrogate-based optimization , 2009 .

[2]  R. Rose,et al.  Flight measurements of the elevator and aileron hinge moment derivates of the Fairey Delta 2 aircraft up to a mach number of 1.6, and comparisons with wind tunnel results , 1965 .

[3]  Shuo Li,et al.  Composite Structural Optimization by Genetic Algorithm and Neural Network Response Surface Modeling , 2005 .

[4]  Ahmed Azouaoui,et al.  On the Computing of the Minimum Distance of Linear Block Codes by Heuristic Methods , 2012, ArXiv.

[5]  J N Nielsen,et al.  Preliminary Method for Estimating Hinge Moments of All-Movable Controls , 1982 .

[6]  Raphael T. Haftka,et al.  Surrogate-based Analysis and Optimization , 2005 .

[7]  Eric V. Anslyn,et al.  Chemosensors: Principles, Strategies, and Applications , 2011 .

[8]  Jure Zupan,et al.  Introduction to Artificial Neural Network (ANN) Methods: What They Are and How to Use Them*. , 1994 .

[9]  S. Sumathi,et al.  Introduction to neural networks using MATLAB 6.0 , 2006 .

[10]  Rakesh K. Kapania,et al.  Control Power Optimization using Artificial Intelligence for Hybrid Wing Body Aircraft , 2015 .

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

[12]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[13]  Rakesh K. Kapania,et al.  Artificial Intelligence Based Control Power Optimization on Tailless Aircraft , 2018 .

[14]  Olympia Roeva,et al.  Real-World Applications of Genetic Algorithms , 2012 .

[15]  Jacques Periaux,et al.  Genetic Algorithms in Engineering and Computer Science , 1996 .

[16]  William P. Rodden,et al.  Application of the Doublet-Lattice Method to Nonplanar Configurations in Subsonic Flow , 1971 .

[17]  T. Rajkumar,et al.  Prediction of Aerodynamic Coefficients Using Neural Networks for Sparse Data , 2002, FLAIRS.

[18]  John Mark,et al.  Introduction to radial basis function networks , 1996 .

[19]  Raúl Rojas Unsupervised Learning and Clustering Algorithms , 1996 .

[20]  G. Gary Wang,et al.  Review of Metamodeling Techniques in Support of Engineering Design Optimization , 2007 .

[21]  Carlos Gershenson,et al.  Artificial Neural Networks for Beginners , 2003, ArXiv.

[22]  A. Jahangirian,et al.  An Efficient Aerodynamic Optimization Method using a Genetic Algorithm and a Surrogate Model , 2007 .

[23]  Jesse R. Quinlan,et al.  Optimization of an Advanced Hybrid Wing Body Concept Using HCDstruct Version 1.2 , 2016 .

[24]  David A. Caughey,et al.  Introduction to Aircraft Stability and Control Course Notes for M&AE 5070 , 2011 .

[25]  Patrick van der Smagt,et al.  Introduction to neural networks , 1995, The Lancet.

[26]  Mark Beale,et al.  Neural Network Toolbox™ User's Guide , 2015 .

[27]  Theodore A. Talay,et al.  Introduction to the Aerodynamics of Flight. NASA SP-367 , 1975 .

[28]  Zhong-Hua Han,et al.  Optimization of Active Flow Control over an Airfoil Using a Surrogate-Management Framework , 2010 .

[29]  Rakesh K. Kapania,et al.  Aeroelastic Applications of a Variable-Geometry Raked Wingtip , 2017 .

[30]  Ke-Shi Zhang,et al.  Coupled Aerodynamic/Structural Optimization of a Subsonic Transport Wing Using a Surrogate Model , 2008 .

[31]  Norman Princen,et al.  CONTROL ALLOCATION CHALLENGES AND REQUIREMENTS FOR THE BLENDED WING BODY , 2000 .

[32]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[33]  S. T. Yousif,et al.  Optimum cost design of reinforced concrete continuous beams using Genetic Algorithms , 2013 .

[34]  Raúl Rojas,et al.  Neural Networks - A Systematic Introduction , 1996 .

[35]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[36]  Richard Kielbasa,et al.  Latin Hypercube Sampling Monte Carlo Estimation of Average Quality Index for Integrated Circuits , 1997 .