×

Control parameter tuning for aircraft crosswind landing via multi-solution particle swarm optimization. (English) Zbl 1523.70008

Summary: Crosswind landing is one of the most complex and challenging landing manoeuvres; an aircraft can drift laterally or could even be blown off the runway under extreme crosswind conditions. This article proposes an improved multi-group swarm-based optimization method that can not only optimize the parameters of the lateral flight control system, but also find diversity solutions of the underlying optimization problem. During the optimizing process, several swarm groups are generated to search potential areas for the optimal solution. These groups exchange information with each other during the searching process and focus on their different but continuous spaces. Finally, a diverse range of solutions is presented to the engineer for the flight control system design. A nonlinear 6 degrees-of-freedom rigid model of a Boeing 747 is used as a test bed to verify the robustness and feasibility of the proposed method.

MSC:

70E55 Dynamics of multibody systems
90C59 Approximation methods and heuristics in mathematical programming
90C90 Applications of mathematical programming
Full Text: DOI

References:

[1] Banks, A.; Vincent, J.; Anyakoha, C., A Review of Particle Swarm Optimization. Part I: Background and Development, Natural Computing, 6, 4, 467-484 (2007) · Zbl 1125.90065 · doi:10.1007/s11047-007-9049-5
[2] Bencheikh, G.; Boukachour, J.; Alaoui, A. E. H., A Memetic Algorithm to Solve the Dynamic Multiple Runway Aircraft Landing Problem, Journal of King Saud University - Computer and Information Sciences, 28, 98-109 (2016) · doi:10.1016/j.jksuci.2015.09.002
[3] Bian, Q.; Zhao, K.; Wang, X.; Xie, R., System Identification Method for Small Unmanned Helicopter Based on Improved Particle Swarm Optimization, Journal of Bionic Engineering, 13, 3, 504-514 (2016) · doi:10.1016/S1672-6529(16)60323-2
[4] Clerc, M., Particle Swarm Optimization (2006), Newport Beach, CA: ISTE, Newport Beach, CA · Zbl 1130.90059
[5] Deng, Y.; Duan, H., Control Parameter Design for Automatic Carrier Landing System via Pigeon-Inspired Optimization, Nonlinear Dynamics, 85, 1, 97-106 (2016) · doi:10.1007/s11071-016-2670-z
[6] Dou, R.; Duan, H., Lévy Flight Based Pigeon-Inspired Optimization for Control Parameters Optimization in Automatic Carrier Landing System, Aerospace Science and Technology, 61, 11-20 (2017) · doi:10.1016/j.ast.2016.11.012
[7] Etkin, B., The Turbulent Wind and Its Effect on Flight, Journal of Aircraft, 18, 5, 327-345 (1981) · doi:10.2514/3.57498
[8] Girish, B. S., An Efficient Hybrid Particle Swarm Optimization Algorithm in a Rolling Horizon Framework for the Aircraft Landing Problem, Applied Soft Computing, 44, 200-221 (2016) · doi:10.1016/j.asoc.2016.04.011
[9] Hanke, C. Rodney; Nordwall, Donald R., The Simulation of a Large Jet Transport Aircraft. Volume 1 - Mathematical Model (1971), Wichita, KS: The Boeing Company, Wichita, KS
[10] Hanke, C. Rodney; Nordwall, Donald R., The Simulation of a Jumbo Jet Transport Aircraft. Volume 2 - Modeling Data (1971), Wichita, KS: The Boeing Company
[11] Hoblit, F. M., Gust Loads on Aircraft: Concepts and Applications (1988), Washington, DC: American Institute of Aeronautics and Astronautics, Washington, DC
[12] Holley, W. E.; Bryson, A. E., Wind Modeling and Lateral Control for Automatic Landing, Journal of Spacecraft and Rockets, 14, 2, 65-72 (1977) · doi:10.2514/3.57163
[13] Jakubcová, M.; Máca, P.; Pech, P., A Comparison of Selected Modifications of the Particle Swarm Optimization Algorithm, Journal of Applied Mathematics, 2014, 1-10 (2014) · Zbl 1437.90166 · doi:10.1155/2014/293087
[14] Kennedy, J.; Eberhart, R. C.; Shi, Y., Swarm Intelligence (2001), San Francisco, CA: Morgan Kaufmann, San Francisco, CA
[15] Li, J.; Duan, H., Simplified Brain Storm Optimization Approach to Control Parameter Optimization in F/A-18 Automatic Carrier Landing System, Aerospace Science and Technology, 42, 187-195 (2015) · doi:10.1016/j.ast.2015.01.017
[16] Lungu, Romulus; Lungu, Mihai, Automatic Landing Control Using H-inf Control and Dynamic Inversion, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 228, 14, 2612-2626 (2014) · Zbl 1305.93040 · doi:10.1177/0954410014523576
[17] Lungu, Romulus; Lungu, Mihai, Application of H2/H∞ Technique to Aircraft Landing Control, Asian Journal of Control, 17, 6, 2153-2164 (2015) · Zbl 1338.93294 · doi:10.1002/asjc.1132
[18] Lungu, Mihai; Lungu, Romulus, Automatic Control of Aircraft Lateral-Directional Motion During Landing Using Neural Networks and Radio-Technical Subsystems, Neurocomputing, 171, 471-481 (2016) · Zbl 1346.93288 · doi:10.1016/j.neucom.2015.06.084
[19] Lungu, Romulus; Lungu, Mihai, Design of Automatic Landing Systems Using the H-inf Control and the Dynamic Inversion, Journal of Dynamic Systems, Measurement and Control, 138, 1-5 (2016) · Zbl 1346.93288
[20] Lungu, Romulus; Lungu, Mihai, Automatic Control of Aircraft in Lateral-Directional Plane During Landing, Asian Journal of Control, 18, 3, 433-446 (2016) · Zbl 1346.93288 · doi:10.1002/asjc.1133
[21] Mutingi, Michael; Mbohwa, Charles, Grouping Genetic Algorithms: Advances and Applications (2016), Cham, Switzerland: Springer, Cham, Switzerland · Zbl 1349.90002
[22] Okamoto, Kazuhide; Tsuchiya, Takeshi, Optimal Aircraft Control in Stochastic Severe Weather Conditions, Journal of Guidance, Control, and Dynamics, 39, 1, 77-85 (2016) · doi:10.2514/1.G001105
[23] Parkinson, B. W.; Spilker, J. J., Global Positioning System: Theory and Applications (1996), Washington, DC: American Institute of Aeronautics and Astronautics, Washington, DC
[24] Roskam, Jan., Airplane Flight Dynamics and Automatic Flight (2001), Lawrence, KS: Design, Analysis and Research Corporation, Lawrence, KS
[25] Stevens, B. L.; Lewis, F. L.; Johnson, E. N., Aircraft Control and Simulation: Dynamics, Controls Design, and Autonomous Systems (2016), Hoboken, NJ: John Wiley & Sons, Hoboken, NJ
[26] Su, Z.; Wang, H., A Novel Robust Hybrid Gravitational Search Algorithm for Reusable Launch Vehicle Approach and Landing Trajectory Optimization, Neurocomputing, 162, 116-127 (2015) · doi:10.1016/j.neucom.2015.03.063
[27] Vepa, Ranjan., Flight Dynamics, Simulation, and Control: For Rigid and Flexible Aircraft (2015), Boca Raton, FL: CRC Press, Boca Raton, FL
[28] Xing, Bo; Gao, Wen-Jing, Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms (2013), Cham, Switzerland: Springer, Cham, Switzerland · Zbl 1330.68004
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.