Multi-Objective Particle Swarm Optimization for Control Laws Design

Abstract:

In order to overcome the difficult of large amount of calculation and to satisfy multiple design indicators in the design of control laws, an improved multi-objective particle swarm optimization (PSO) algorithm was used to design control laws of aircraft. Firstly, the hybrid concepts of genetic algorithm were introduced to particle swarm optimization (PSO) algorithm to improve the algorithm. Then based on aircraft flying quality the reference models were built, and then the tracking error, settling time and overshoot were used as the optimization goal of the control laws design. Based on this multi-objective optimize problem the attitude hold control laws were designed. The simulation results show the effectiveness of the algorithm.

• Periodical: Applied Mechanics and Materials (Volumes 333-335)
• Pages: 1361-1365
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.333-335.1361
• Online since: July 2013
• Authors: Xiao Xiong Liu, Heng Xu, Yan Wu, Peng Hui Li
• Keywords: Attitude Hold Control, Genetic Hybrid, Multi-Objective Particle Swarm Optimization

References

[1] Xiangjun Meng, Dynamic Multilevel Optimization of Machine Design and Control Parameters Based on Correlation Analysis, IEEE Transactions on magnetics, 2010, 46(8): 26-35.

[2] Fangzhen Song, Hui Liu, Bo Song, Huimin Feng, Dynamic Optimization of PID Control Parameters of Complex Magnetic Suspension Electromechanical Coupling System, Proceedings of the 8th World Congress on Intelligent Control and Automation, Jinan, China, July 6-9, (2010).

DOI: 10.1109/wcica.2010.5553810

[3] K. Tang, B. Wang, W. Kang, B. Chen, Minimum Time Control of Helicopter UAVs using Computational Dynamic Optimization, 2011 9th IEEE International Conference on Control and Automation (ICCA), Santiago, Chile, December 19-21, (2011).

DOI: 10.1109/icca.2011.6137973

[4] Li Guangwen, Jia Qiuling, Zhang Weiguo, Liu Xiaoxiong, Gain scheduling method of aircraft control law in the large flight envelope, Flight Dynamics, 2010, 28(1): 29-31.

[5] Nie Rui, Zhang Weiguo, Li Guangwen, Liu Xiaoxiong, Optimization of aircraft flight control system based on improved multi-objective evolutionary algorithm, Application Research of Computers, 2011, 28(5): 1703-1706.

DOI: 10.1109/icicisys.2009.5357893

[6] Y. Shi, R. Eberhart, C. Empirical, Study of Particle swarm optimization[C], Proceeding of the World Multiconference on Syatemics. Cybernetics and Informatics, Orlando, 2000: 1945-(1950).

[7] Joshua Knowles, David Corne, Approximating the non-dominated front using the Pareto Archived Evolution Strategy, Evolutionary Compution, 2000, 8(2): 149-172.

DOI: 10.1162/106365600568167

[8] Coello C A, Lechuga M S, MOPSO: a proposal for multiple objective particle swarm optimization[C], Proceedings of the Congress on Evolutionary Computation. Piscataway: IEEE, Service Center, 2002: 1051-1056.

DOI: 10.1109/cec.2002.1004388

[9] Leong W F, Yen G G, Dynamic swarms in PSO-based multi-objective optimization[C], IEEE Congress on Evolutionary Computation Singapore: IEEE, 2007: 3172-3179.

DOI: 10.1109/cec.2007.4424877

[10] Balling R, The maximin fitness function Multi-objective city and regional planning[C], Proc of the 2nd Int Conf on Evolutionary Multi-criterion Optimization, 2003: 1-15.

DOI: 10.1007/3-540-36970-8_1