复合式旋翼飞行器多目标控制分配策略

doi:10.7527/S1000-6893.2019.22727

电子电气工程与控制

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复合式旋翼飞行器多目标控制分配策略

郑峰婴¹, 刘龙武¹, 程月华¹, 陈志明¹, 成锋娜²

1. 南京航空航天大学航天学院, 南京 210016;
2. 南京林业大学机械电子工程学院, 南京 210037

收稿日期:2018-10-15 修回日期:2018-11-26 出版日期:2019-06-15 发布日期:2019-03-13
通讯作者: 郑峰婴 E-mail:zhfy@nuaa.edu.cn
基金资助:
国家自然科学基金（61803200，61673206）；中央高校基本科研业务费专项资金（NZ2016111）；国防科技重点实验室基金（6142220180304）

Multi-objective control allocation strategy of compound rotorcraft

ZHENG Fengying¹, LIU Longwu¹, CHENG Yuehua¹, CHEN Zhiming¹, CHENG Fengna²

1. College of Aeronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
2. College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China

Received:2018-10-15 Revised:2018-11-26 Online:2019-06-15 Published:2019-03-13
Supported by:
National Natural Science Foundation of China (61803200,6163206);the Fundamental Research Funds for the Central Universities(NZ2016111);National Defense Science and Technology Fundation of Key Laborory (6142220180304)

摘要/Abstract

摘要： 针对复合式旋翼飞行器操纵冗余多模式切换控制问题，提出一种基于赋权多目标混合优化的控制分配策略。该策略根据复合式旋翼飞行器过渡模式舵面操纵特性，建立飞行器带约束过渡过程控制分配模型；设计混合多目标优化性能指标评价函数，有效处理操纵量控制受限、交叉强耦合及非线性特性，并减少舵面耗能；采用改进的粒子群优化算法动态更新操纵量及控制通道的权系数矩阵，提高控制面操纵效率，加快优化搜索速度，快速求解过渡过程多目标控制分配变量。该策略实现复合式旋翼飞行器模式切换过渡过程实时有效地操纵量控制分配，保证飞行器快速准确跟踪控制指令的能力。同时，通过多目标控制分配策略，飞行控制系统不需要增加额外的模式切换控制器，降低系统设计难度，提高安全性。

关键词: 复合式旋翼飞行器, 模式切换, 操纵冗余, 多目标优化, 控制分配, 粒子群算法

Abstract: Aiming at the control plane redundancy of the compound rotorcraft in multi-mode transition process, a control allocation strategy based on weighted multi-objective hybrid optimization is proposed. Drawing on the characteristics of control surfaces in the transition mode, a control allocation model for aircraft with constrained transition procedure is established. A hybrid multi-objective optimization performance index evaluation function is designed, which effectively handle the limitation, the cross strong coupling, and the nonlinear characteristics of control surfaces, and reduces the energy consumption. The improved particle swarm optimization algorithm is used to dynamically update the weight coefficient matrixes of the steering surface manipulation and the control channels, improve the control surface control efficiency, speed up the optimization of the search speed, and quickly solve the steering surface manipulation of the multi-object control allocation in the transition mode. The strategy realizes the real-time effective steering surface control allocation of the compound rotorcraft in the mode switching transition process, ensuring the ability of the aircraft to quickly and accurately track the control commands. At the same time, the multi-objective control allocation makes it unnecessary to extra conversion controllers in the transition mode, reducing the difficulty of the flight control system design and improving the system security.

Key words: compound rotorcraft, mode transition, redundant control, multi-objective optimization, control allocation, particle swarm optimization

中图分类号:

V275⁺.1

郑峰婴, 刘龙武, 程月华, 陈志明, 成锋娜. 复合式旋翼飞行器多目标控制分配策略[J]. 航空学报, 2019, 40(6): 322727-322727.

ZHENG Fengying, LIU Longwu, CHENG Yuehua, CHEN Zhiming, CHENG Fengna. Multi-objective control allocation strategy of compound rotorcraft[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(6): 322727-322727.

参考文献

[1] PASSE B, SRIDHARAN A, BAEDER J. Computational investigation of coaxial rotor interactional aerodynamics in steady forward flight[C]//33rd AIAA Applied Aerodynamics Conference. Reston, VA:AIAA, 2015:29.
[2] ZHAO Y, LI X, SHI Y, et al. Analysis on rotor-propellers interaction flowfield for compound double-thust-propeller high-speed helicopters[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2017, 49(2):154-164.
[3] REDDINGER J, GANDHI F. Physics-based trim optimization of an articulated slowed-rotor compound helicopter in high-speed flight[J]. Journal of Aircraft, 2015, 52(6):1756-1766.
[4] HERSEY S, SRIDHARAN A, CELI R. Multiobjective performance optimization of a coaxial compound rotorcraft configuration[J]. Journal of Aircraft, 2017, 54(4):1498-1507.
[5] REDDINGER J, GANDHI F, KANG H. Using control redundancy for power and vibration reduction on a compound helicopter at high speeds[J]. Journal of The American Helicopter Society, 2018, 63(3):1-13.
[6] JIANG X, SU C, XU Y, et al. An adaptive backstepping sliding mode method for flight attitude of quadrotor UAVs[J]. Journal of Central South University, 2018, 25(3):616-631.
[7] BOSKOVIC J D, MEHRA R K. Control allocation in overactuated aircraft under position and rate limiting[C]//Proceedings of the American Control Conference. Piscataway, NJ:IEEE Press, 2002:791-796.
[8] BODSON M. Evaluation of optimization methods for control allocation[J]. Journal of Guidance, Control, and Dynamics, 2002, 25(4):703-711.
[9] PETERSEN J, BODSON M. Constrained quadratic programming techniques for control allocation[J]. IEEE Transactions on Control Systems Technology, 2006, 14(1):91-98.
[10] DOMAN D, OPPENHEIMER M. Improving control allocation accuracy for nonlinear aircraft dynamics[C]//AIAA Guidance, Navigation, and Control Conference and Exhibit. Reston, VA:AIAA, 2002.
[11] BOLENDER M A, DOMAN D B. Nonlinear control allocation using piecewise linear functions:A linear programming approach[J]. Journal of Guidance, Control, and Dynamics, 2005, 28(3):558-562.
[12] HU Q, LI B, ZHANG Y. Nonlinear proportional-derivative control incorporating closed-loop control allocation for spacecraft[J]. Journal of Guidance, Control, and Dynamics, 2014, 37(3):799-812.
[13] CRISTOFARO A, POLYCARPOU M M, JOHANSEN T A. Fault-tolerant control allocation for overactuated nonlinear systems[J]. Asian Journal of Control, 2018, 20(2):621-634.
[14] PEDRO J O, TSHABALALA T B. Fault-tolerant control of fixed-wing UAV using GA-optimised control allocation technique[C]//2017 11th ASian Control Conference (ASCC). Piscataway, NJ:IEEE Press, 2017:371-376.
[15] HAMAYUN M T, EDWARDS C, ALWI H. Design and analysis of an integral sliding mode fault-tolerant control scheme[J]. IEEE Transactions on Automatic Control, 2012, 57(7):1783-1789.
[16] 路遥, 董朝阳, 王青, 等. 存在整数约束的分布式驱动变体飞行器控制分配[J]. 控制理论与应用, 2018, 35(8):1083-1091 LU Y, DONG C Y, WANG Q, et al. Control allocation for distributed driving morphing aircraft with integer constraints[J]. Control Theory & Applications, 2018, 35(8):1083-1091(in Chinese).
[17] BUFFINGTON J. Tailless aircraft control allocation[C]//Guidance, Navigation, and Control Conference, Guidance, Navigation, and Control and Co-located Conferences, Reston, VA:AIAA, 1997.
[18] 杨恩泉, 高金源, 李卫琪. 多目标非线性控制分配方法研究[J]. 航空学报, 2008, 29(4):995-1001 YANG E Q, GAO J Y, LI W Q. Research on multi-object nonlinear control allocation[J]. Acta Aeronautica et Astronuatica Sinica, 2008, 29(4):995-1001(in Chinese).
[19] 贾瑞, 吴梅. 基于遗传算法的控制分配在飞艇中的应用[J]. 飞行力学, 2014, 32(4):364-367 JIA R, WU M. Application of genetic algorithm based control allocation in airship[J]. Flight Dynamics, 2014, 32(4):364-367(in Chinese).
[20] CHOLLOM T D, OFODILE N, UBADIKE O. Application techniques of multi-objective particle swarm optimization:Aircraft flight control[C]//2016 UKACC 11th International Conference on Control (CONTROL). Piscataway, NJ:IEEE Press, 2016.
[21] LEE J H, BYOUNG-MUN M, EUNG-TAI K. Autopilot design of tilt-rotor UAV using particle swarm optimization method[C]//2007 International Conference on Control, Automation and Systems. Piscataway, NJ:IEEE Press, 2007.
[22] 徐冠峰, 陈铭. 小型共轴式直升机旋翼桨叶铰链力矩研究[J]. 航空动力学报, 2010, 25(8):1805-1810. XU G F, CHEN M. Research on rotor blade hinge moment of a small-scale coaxial helicopter[J]. Journal of Aerospace Power, 2010, 25(8):1805-1810(in Chinese).
[23] CHEN R TN, LEBACQZ J V, AIKEN E W. Helicopter mathematical models and control law development for handing qualities:NASA-CR-249[R]. Washington, D.C.:NASA, 1988.
[24] 蒋鸿翔, 徐锦法, 高正, 等. 新型复合式无人直升机悬停/着陆控制[J]. 航空学报, 2008, 29(S1):46-54. JIANG H X, XU J F, GAO Z, et al. Hover/landing control for the novel compound unmanned aerial helicopter[J]. Acta Aeronautica et Astronuatica Sinica, 2008, 29(S1):46-54(in Chinese).
[25] 陈仁良, 李攀, 吴伟, 等. 直升机飞行动力学数学建模问题[J]. 航空学报, 2017, 38(7):620915. CHEN R L, LI P, WU W, et al. A review of mathematical modeling of helicopter flight dynamics[J]. Acta Aeronautica et Astronuatica Sinica, 2017, 38(7):6-22(in Chinese).
[26] 韩敏, 何泳. 基于高斯混沌变异和精英学习的自适应多目标粒子群算法[J]. 控制与决策, 2016, 31(8):1372-1378. HAN M, HE B. Adaptive multi-objective particle swarm optimization with Gaussian chaotic mutation elite learning[J]. Control and Decision, 2016, 31(8):1372-1378(in Chinese).

E-mail：hkxb@buaa.edu.cn

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

复合式旋翼飞行器多目标控制分配策略

Multi-objective control allocation strategy of compound rotorcraft

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