In this article, a yaw channel controller with linear active disturbance rejection control is proposed for high-performance attitude tracking of an unmanned helicopter. In this control scheme, the linear extended state observer serves as a compensator, which can effectively reject parametric uncertainties and external disturbances. In this case, the attitude tracking controller is designed according to the input–output relation of yaw model. As a controlled plant, the yaw dynamical model is first obtained through the system identification method. Subsequently, in order to optimize the linear active disturbance rejection control applied to yaw control, an artificial bee colony algorithm is introduced to solve the problem of parameter tuning. Finally, comparative studies are carried out. The results from simulation and flight experiment show that the proposed controller provides better performances than the traditional controller.
AboudoniaAEl-BadawyARashadR. Disturbance observer-based feedback linearization control of an unmanned quadrotor helicopter. Proc IMechE, Part I: J Systems and Control Engineering2016; 230(9): 877–891.
2.
GandolfoDCSalinasLRBrandãoAS. Path following for unmanned helicopter: an approach on energy autonomy improvement. Inf Technol Control2016; 45(1): 86–98.
3.
ShengSSunC. Yaw control of an unmanned helicopter using adaptive model feedback and error compensation. J Aerospace Eng2015; 29(2): 06015002.
4.
KoCCChengBMChenJ. A web-based laboratory on control of a two-degrees-of-freedom helicopter. Int J Eng Educ2005; 21(6): 1017.
5.
ShinJNonamiKFujiwaraD. Model-based optimal attitude and positioning control of small-scale unmanned helicopter. Robotica2005; 23(1): 51–63.
6.
WangBChenBMLeeTH. An RPT approach to time-critical path following of an unmanned helicopter. In: Proceedings of the 8th Asian control conference (ASCC), Kaohsiung, Taiwan, 15–18 May 2011, pp.211–216. New York: IEEE.
7.
GadewadikarJLewisFSubbaraoK. Structured H-infinity command and control-loop design for unmanned helicopters. J Guid Control Dynam2008; 31(4): 1093–1102.
8.
CastilloCLMorenoWValavanisKP. Unmanned helicopter waypoint attitude tracking using model predictive control. In: Proceedings of the Mediterranean conference on control & automation (MED’07), Athens, 27–29 June 2007, pp.1–8. New York: IEEE.
9.
RaptisIAValavanisKPMorenoWA. A novel nonlinear backstepping controller design for helicopters using the rotation matrix. IEEE T Contr Syst T2011; 19(2): 465–473.
10.
BijnensBChuQPVoorsluijsGM. Adaptive feedback linearization flight control for a helicopter UAV. In: Proceedings of the AIAA guidance, navigation, and control conference and exhibit, San Francisco, CA, 15–18 August 2005, pp.15–18. Reston, VA: AIAA.
11.
KannanSKJohnsonEN. Adaptive attitude based control for autonomous helicopters. In: Proceedings of the 21st digital avionics systems conference, vol. 2, Irvine, CA, 27–31 October 2002. New York: IEEE.
12.
SongJGanZHanJ. Study of active disturbance rejection controller on filtering. Control Decis2003; 18(1): 110–112.
13.
GaoZ. Active disturbance rejection control: a paradigm shift in feedback control system design. In: Proceedings of the 2006 American control conference, Minneapolis, MN, 14–16 June 2006, pp.2399–2405. New York: IEEE.
14.
ZhangJFengJZhouY. Linear active disturbance rejection control of waste heat recovery systems with organic Rankine cycles. Energies2012; 5(12): 5111–5125.
15.
GaoJZhuangYXiaoJ. Attitude tracking control of a quadrotor based on linear active disturbance rejective control. In: Proceedings of the 2015 IEEE international conference on CYBER technology in automation, control, and intelligent systems (CYBER), Shenyang, China, 8–12 June 2015, pp.287–292. New York: IEEE.
16.
LiuLXuZMeiQ. Induction motor drive system based on the flux observer LESO and LADRC. In: Proceedings of the 2009 WRI global congress on intelligent systems, vol. 2, Xiamen, China, 19–21 May 2009, pp.248–252. New York: IEEE.
17.
KarabogaD. An idea based on honey bee swarm for numerical optimization. Technical report-tr06, 2005. Kayseri, Turkey: Computer Engineering Department, Engineering Faculty, Erciyes University.
18.
DingLWuHYaoY. Dynamic model identification for 6-DOF industrial robots. J Robot2015; 1–9.
19.
TijaniIBAkmeliawatiRLegowoA. Hybrid DE-PEM algorithm for identification of UAV helicopter. Aircr Eng Aerosp Tec2014; 86(5): 385–405.
20.
DuJZhangYLüT. Unmanned helicopter flight controller design by use of model predictive control. WSEAS T Syst2008; 7(2): 81–87.
21.
MettlerBKanadeTTischlerMB. System identification modeling of a model-scale helicopter. Pittsburgh, PA: The Robotics Institute, Carnegie Mellon University, 2000.
22.
KimHCDharmayandaHRKangT. Parameter identification and design of a robust attitude controller using H∞ methodology for the raptor E620 small-scale helicopter. Int J Control Autom2012; 10(1): 88–101.
23.
GoforthFJZhengQGaoZ. A novel practical control approach for rate independent hysteretic systems. ISA Trans2012; 51(3): 477–484.
24.
XuCDuanHLiuF. Chaotic artificial bee colony approach to Uninhabited Combat Air Vehicle (UCAV) path planning. Aerosp Sci Technol2010; 14(8): 535–541.
25.
DingLWuHYaoY. Chaotic artificial bee colony algorithm for system identification of a small-scale unmanned helicopter. Int J Aerosp Eng2015; 2015: 1–12.
26.
GaoZ. Scaling and bandwidth-parameterization based controller tuning. In: Proceedings of the American control conference, vol. 6, Minneapolis, MN, 14–16 June 2006, pp.4989–4996. New York: IEEE.
27.
GaingZL. A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE T Energy Conver2004; 19(2): 384–391.
28.
CivelekZÇamELüyM. Proportional–integral–derivative parameter optimisation of blade pitch controller in wind turbines by a new intelligent genetic algorithm. IET Renew Power Gen2016; 10(8): 1220–1228.
29.
BoneGMXueMFlettJ. Position control of hybrid pneumatic–electric actuators using discrete-valued model-predictive control. Mechatronics2015; 25: 1–10.
30.
AbachizadehMYazdiMRHYousefi-KomaA. Optimal tuning of PID controllers using artificial bee colony algorithm. In: Proceedings of the 2010 IEEE/ASME international conference on advanced intelligent mechatronics, Montreal, QC, Canada, 6–9 July 2010, pp.379–384. New York: IEEE.
31.
GadewadikarJLewisFLSubbaraoK. H-infinity static output-feedback control for rotorcraft. J Intell Robot Syst2009; 54(4): 629–646.
32.
BudiyonoAWibowoSS. Optimal tracking controller design for a small scale helicopter. J Bionic Eng2007; 4(4): 271–280.
