There has been a renewed interest in recent times in airship technology owing to its potential usage for applications ranging from defense, scientific exploration, advertising to even remote monitoring. For airships to expand operational profile, further enhancement of configurational features and control development for full autonomy are key technologies gaining attention. In this paper, beginning with the mathematical modeling of a thrust-vectored airship, the integrated motion planning and controller development for vehicle autonomy, taking into account various uncertainties, are dealt with. A rapidly exploring random tree-based obstacle avoidance path planning exercise is carried out to chart out a trajectory in the presence of obstacles. Then, a neural network-based sliding mode controller is subsequently designed that learns the unknown equivalent control in sliding mode control framework to track the reference trajectory. Simulation results presented at the end demonstrate the effectiveness of the approach.
ZhangXLiangHZhaoM, et al.Mode analysis of the thrust-vectored system on an airship. AIAA J2018; 56: 4199–4204.
2.
Zhang X, Liang H, Guo X, et al. Several concerns of advanced airships for thrust-vectoring application on dynamic study. In: 23rd AIAA lighter-than-air systems technology conference, Denver, CO, USA, 5–9 June 2017, p.3992.
3.
Benjovengo FP, Paiva E, Bueno SS, et al. Sliding mode control approaches for an autonomous unmanned airship. In: 18th AIAA lighter-than-air systems technology conference, Seattle, Washington, 4–7 May 2009.
4.
YangY. A time-specified nonsingular terminal sliding mode control approach for trajectory tracking of robotic airships. Nonlinear Dyn2018; 92: 1359–1367.
5.
YangY. Positioning control for stratospheric satellites subject to dynamics uncertainty and input constraints. Aerosp Sci Technol2019; 86: 534–541.
6.
Azinheira JR, de Paiva EC, Ramos J, et al. Mission path following for an autonomous unmanned airship. In: Robotics and automation, 2000. Proceedings. IEEE international conference on ICRA'00. vol. 2. New York, NY: IEEE, pp.1269–1275.
7.
Lee SJ, Kim DM and Bang HC. Feedback linearization controller for semi station keeping of the unmanned airship. In: AIAA 5th ATIO and16th lighter-than-air system technology and balloon systems conferences, Arlington, Virginia, 26–28 September 2005. p.7343.
8.
de Paiva E, Benjovengo F, Bueno S, et al. Nonlinear control approaches for an autonomous unmanned robotic airship. In: 7th AIAA ATIO conference, 2nd CEIAT international conference 17th LTA systems technical conference on innovation and integration in aero sciences; followed by 2nd TEOS forum, Belfast, Northern Ireland, 18–20 September 2007, p.7782.
9.
Acosta D and Joshi S. Adaptive nonlinear dynamic inversion control of an autonomous airship for the exploration of titan. In: AIAA guidance, navigation and control conference and exhibit, Hilton Head, South Carolina, 20–23 August 2007, p.6502.
10.
Mishra A and Sinha N. Design of adaptive sliding mode control for uncertain thrust vectored airship. In: 2018 AIAA guidance, navigation, and control conference, Kissimmee, FL, USA, 8–12 January 2018, p.1129.
11.
Park CS, Lee H, Tahk MJ, et al. Airship control using neural network augmented model inversion. In: Control applications, 2003. Proceedings of 2003 IEEE conference on CCA 2003. vol. 1. New York, NY: IEEE, pp.558–563.
12.
Yang Y and Yan Y. Trajectory tracking for robotic airships using sliding mode control based on neural network approximation and fuzzy gain scheduling. Proc IMechE, Part I: J Systems and Control Engineering2015; 230: 184–196.
13.
Kim KJ, Park JB and Choi YH. Chattering free sliding mode control. In: SICE-ICASE, 2006. international joint conference. New York, NY: IEEE, pp.732–735.
14.
Slotine JJE, Li W, et al. Applied nonlinear control. vol. 199. Englewood Cliffs, NJ: Prentice Hall, 1991.
15.
YangYYanY. Trajectory tracking for robotic airships using sliding mode control based on neural network approximation and fuzzy gain scheduling. Proc IMechE, Part I: J Systems and Control Engineering2016; 230: 184–196.
16.
YangYYanY. Attitude regulation for unmanned quadrotors using adaptive fuzzy gain-scheduling sliding mode control. Aerosp Sci Technol2016; 54: 208–217.
17.
ShiW. Adaptive fuzzy control for mimo nonlinear systems with nonsymmetric control gain matrix and unknown control direction. IEEE Trans Fuzzy Syst2014; 22: 1288–1300.
18.
LillyJH. Fuzzy control and identification, Hoboken, NJ: John Wiley & Sons, 2011.
19.
KavrakiLESvestkaPLatombeJC, et al.Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans Robot Autom1996; 12: 566–580.
20.
KaramanSFrazzoli \E. Sampling-based algorithms for optimal motion planning. Int J Robot Res2011; 30: 846–894.
21.
LaValleSMKuffnerJJ. Randomized kinodynamic planning. Int J Robot Res2001; 20: 378–400.
22.
JansonLSchmerlingEClarkA, et al.Fast marching tree: a fast marching sampling-based method for optimal motion planning in many dimensions. Int J Robot Res2015; 34: 883–921.
23.
Sinha NK and Ananthkrishnan N. Elementary flight dynamics with an introduction to bifurcation and continuation methods. Boca Raton, FL: CRC Press, 2013.
24.
Lavalle SM. Rapidly-exploring random trees: a new tool for path planning, Iowa State University, Ames, IA, USA, 1998. Citeseer.
25.
Jaillet L, Yershova A, La Valle S, et al. Adaptive tuning of the sampling domain for dynamic-domain RRTS. New York, NY: IEEE, 2005, pp.2851–2856.
26.
Breivik M and Fossen TI. Principles of guidance-based path following in 2D and 3D. In: Decision and control, 2005 and 2005 European control conference. CDC-ECC'05. 44th IEEE conference on IEEE, pp.627–634. New York, NY: IEEE.
27.
Lekkas AM and Fossen TI. A time-varying lookahead distance guidance law for path following. IFAC Proc Vol 2012; 45: 398–403.
28.
Dybsjord KA. Fault-tolerant UAV flight control system. Master's Thesis, Institutt for Teknisk Kybernetikk, Norway, 2013.
29.
KhatriAKSinghJSinhaNK. Aircraft maneuver design using bifurcation analysis and sliding mode control techniques. J Guid Control Dyn2012; 35: 1435–1449.
30.
XuHMirmiraniMDIoannouPA. Adaptive sliding mode control design for a hypersonic flight vehicle. J Guid Control Dyn2004; 27: 829–838.
31.
Ding Z. Nonlinear and adaptive control systems. vol. 84. Stevenage, UK: IET, 2013.
32.
SlotineJJCoetseeJ. Adaptive sliding controller synthesis for non-linear systems. Int J Control1986; 43: 1631–1651.
33.
Liu J and Wang X. Advanced sliding mode control for mechanical systems: design, analysis and MATLAB simulation. Berlin, Germany: Springer Science & Business Media, 2012.
34.
El-SousyFF. Adaptive hybrid control system using a recurrent RBFN-based self-evolving fuzzy-neural-network for PMSM servo drives. Appl Soft Comput2014; 21: 509–532.
35.
DevikaKThomasS. Power rate exponential reaching law for enhanced performance of sliding mode control. Int J Control Autom Syst2017; 15: 2636–2645.
36.
Sinha NK and Ananthkrishnan N. Advanced flight dynamics with elements of flight control. Boca Raton, FL: CRC Press, 2017.
