基于柯西变异鸽群优化的大型民用飞机滚动时域控制
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摘要
采用先进的控制方法对大型民用飞机的多操纵面进行控制,可以有效提高大型民用飞机飞行的安全性和可靠性.针对大型民用飞机的在线滚动时域控制(receding horizon control,RHC)问题,提出了柯西变异鸽群优化(Cauthy mutation pigeon inspired optimization,CMPIO).柯西变异鸽群优化不但保持了鸽群优化(pigeon inspired optimization,PIO)收敛速度快的优点,而且通过执行加入柯西变异的地图和指南针算子和地标算子,可以有效降低优化结果陷入局部最优的概率,改善滚动时域控制的快速性和稳定性.在协调转弯过程和协调转弯的故障重构两个算例的仿真中,基于柯西变异鸽群优化的滚动时域控制使大型飞机稳定地达到参考状态,控制器同时合理地完成了多操纵面的控制分配和故障重构.
Multi-control surfaces controlled by advanced control methods can improve the safety and reliability of large civil aircraft effectively.An on-line optimization algorithm named Cauthy mutation pigeon inspired optimization(CMPIO) is proposed in this paper to solve the receding horizon control(RHC) problem. CMPIO preserves the obvious advantage of pigeon inspired optimization(PIO), which has fast convergence rate. Besides, CMPIO not only reduces the probability of optimization results trapping into local optimum, but enhances the rapidity and reliability of RHC by utilizing the map and compass operator and landmark operator improved by Cauthy mutation. In the simulations of coordinated turn and fault reconstruction in coordinated turn, it is shown that the large civil aircraft reaches reference state quickly and stably in RHC based on CMPIO. Meanwhile, controller completes the control allocation of multiple-control surfaces legitimately.
Multi-control surfaces controlled by advanced control methods can improve the safety and reliability of large civil aircraft effectively.An on-line optimization algorithm named Cauthy mutation pigeon inspired optimization(CMPIO) is proposed in this paper to solve the receding horizon control(RHC) problem. CMPIO preserves the obvious advantage of pigeon inspired optimization(PIO), which has fast convergence rate. Besides, CMPIO not only reduces the probability of optimization results trapping into local optimum, but enhances the rapidity and reliability of RHC by utilizing the map and compass operator and landmark operator improved by Cauthy mutation. In the simulations of coordinated turn and fault reconstruction in coordinated turn, it is shown that the large civil aircraft reaches reference state quickly and stably in RHC based on CMPIO. Meanwhile, controller completes the control allocation of multiple-control surfaces legitimately.
引文
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3 Gong H,Zhen Z,Li X,et al.Design of automatic climbing controller for large civil aircraft.J Franklin I,2013,350:2442–2454
4 Wang X,Wang S,Yang Z,et al.Active fault-tolerant control strategy of large civil aircraft under elevator failures.Chin J Aeronaut,2015,28:1658–1666
5 Ríos H,Kamal S,Fridman L M,et al.Fault tolerant control allocation via continuous integral sliding-modes:A HOSM-observer approach.Automatica,2015,51:318–325
6 Dong Y,Zhang Y,Ai J.Full-altitude attitude angles envelope and model predictive control-based attitude angles protection for civil aircraft.Aerosp Sci Tech,2016,55:292–306
7 Keviczky Tá,Borrelli F,Fregene K,et al.Decentralized receding horizon control and coordination of autonomous vehicle formations.IEEE Trans Contr Syst Technol,2008,16:19–33
8 Bellingham J,Richards A,How J P.Receding horizon control of autonomous aerial vehicles.In:Proceedings of the American Control Conference.Anchorage,2002.3741–3746
9 Schouwenaars T,How J,Feron E.Receding horizon path planning with implicit safety guarantees.In:Proceedings of the American Control Conference.Boston,2004.5576–5581
10 Kuwata Y,Schouwenaars T,Richards A,et al.Robust constrained receding horizon control for trajectory planning.In:AIAA Guidance,Navigation,and Control Conference and Exhibit.San Francisco,2005.6079
11 Watterson M,Kumar V.Safe receding horizon control for aggressive MAV flight with limited range sensing.In:2015 IEEE/RSJ International Conference on Intelligent Robots and Systems.Hamburg:IEEE,2015.3235–3240
12 Zhan Z H,Zhang J,Li Y,et al.An efficient ant colony system based on receding horizon control for the aircraft arrival sequencing and scheduling problem.IEEE Trans Intell Transport Syst,2010,11:399–412
13 Looye G,Joos H D.Design of robust dynamic inversion control laws using multi-objective optimization.In:AIAA Guidance,Navigation,and Control Conference and Exhibit.Montreal,2001.4285
14 张祥银,段海滨,余亚翔.基于微分进化的多UAV紧密编队滚动时域控制.中国科学:信息科学,2010,40:569–582
15 Qiu H,Duan H.Receding horizon control for multiple UAV formation flight based on modified brain storm optimization.Nonlinear Dynam,2014,78:1973–1988
16 Duan H,Qiao P.Pigeon-inspired optimization:A new swarm intelligence optimizer for air robot path planning.Int Jnl Intel Comp Cyber,2014,7 :24–37
17 柴树梁.大型民用飞机控制分配与自适应重构算法研究.硕士学位论文.上海:上海交通大学,2013
18 Hanke C R,Nordwall D R.The simulation of a jumbo jet transport aircraft Volume 2:Modeling data.NASA Technical Report,1970