Motion planning and trajectory control are two basic challenges of unmanned vehicles. In motion planning problem, feasible trajectories are developed while nonlinear dynamic model and performance constraints of the vehicle under utility are considered. In this study, motion planning is performed via an enhanced particle swarm optimization algorithm. The resulting offline generated trajectories are tracked using a nonlinear trajectory control system methodology. The Lyapunov-based constrained backstepping approach and command filters are utilized in designing the trajectory control system. Command filters smoothen the input signals and provide their derivatives. Evaluation of the proposed integrated approach in several simulated scenarios has effectively demonstrated the potential of both algorithms in generating optimal contour matching trajectories as well as excellent tracking capability of the trajectory control system.
Twigg S, Calise AJ and Johnson EN. On-line trajectory optimization including moving threats and targets. In: Proceedings of the AIAA guidance, navigation, and control conference, Providence, RI, 16–19 August 2004, pp.807–813. Reston, VA: AIAA.
2.
RoysetJOCarlyleWMWoodRK. Routing military aircraft with a constrained shortest–path algorithm. Mil Oper Res2009; 14(3): 31–52.
3.
Kuffner JJ and LaValle SM. RRT-connect: an efficient approach to single-query path planning. In: Proceedings of the IEEE international conference on robotics and automation (ICRA’2000), San Francisco, CA, 24–28 April 2000, pp.995–1001. Piscataway, NJ: IEEE.
4.
Jian D and Vagners J. Parallel evolutionary algorithms for UAV path planning. In: Proceedings of the AIAA 1st intelligent system technical conference, Chicago, IL, 20–22 September 2004, pp.1499–1506. Reston, VA: AIAA.
5.
GuanjunMHaibinDSenqiL. Improved ant colony algorithm for global trajectory planning of UAV under complex environment. Int J Comput Sci Appl2007; 4(3): 57–68.
6.
Frazzoli E and Bullo F. On quantization and optimal control of dynamical systems with symmetries. In: Proceedings of the IEEE conference on decision and control, Las Vegas, NV, 10–13 December 2002, vol. 1, pp.817–823. Piscataway, NJ: IEEE.
7.
FrazzoliEDahlehMAFeronE. Maneuver-based motion planning for nonlinear systems with symmetries. IEEE Trans Rob2005; 21(6): 1077–1091.
8.
Frazzoli E. Robust hybrid control for autonomous vehicle motion planning. PhD Thesis, Massachusetts Institute of Technology, Cambridge, 2001.
9.
Krenzke T. Ant colony optimization for agile motion planning. Master’s Thesis, Massachusetts Institute of Technology, Aerospace Engineering, 2006.
10.
HealyALiebardD. Multivariable sliding mode control for autonomous diving and steering of unmanned underwater vehicles. IEEE J Oceanic Eng1993; 18(3): 327–339.
11.
NarasimhanMDongHMittalR. Optimal yaw regulation and trajectory control of biorobotic AUV using mechanical fins based on CFD parametrization. Trans ASME J Fluids Eng2006; 128(4): 687–698.
12.
RenWBeardRW. Trajectory tracking for unmanned air vehicles with velocity and heading rate constraints. IEEE Trans Control Syst Technol2004; 12(5): 706–716.
PachterMD’AzzoJJProudAW. Tight formation control. J Guid Control Dyn2001; 24(2): 246–254.
15.
KaminerIPascoalAHallbergE. Trajectory tracking for autonomous vehicles: an integrated approach to guidance and control. J Guid Control Dyn1998; 21(1): 29–38.
16.
SinghSNSteinbergMLPageAB. Nonlinear adaptive and sliding mode flight path control of FA/18 model. IEEE Trans Aerosp Electron Syst2003; 39(4): 1250–1263.
17.
SonneveldtLChuQPMulderJA. Nonlinear flight control design using constrained adaptive backstepping. J Guid Control Dyn2007; 30(2): 322–336.
18.
FarrellJSharmaMPolycarpouM. Backstepping based flight control with adaptive function approximation. AIAA J Guid Control Dyn2005; 28(6): 1089–1102.
19.
SonneveldtLOort vanERChuQP. Nonlinear adaptive trajectory control applied to an F-16 model. J Guid Control Dyn2009; 32(1): 25–39.
20.
StevensBLLewisFL. Aircraft control and simulation. New York, NY: John Wiley & Sons, Inc, 1992.
21.
Karimi J and Pourtakdoust SH. Optimal performing of flight maneuvers for a UAV utilizing a Heuristic constrained optimization approach. In: Proceedings of the 9th Iranian aerospace society conference, AERO 2010, Islamic Azad University, Science and Research Branch, Tehran, Iran, 8–10 February 2010 (in Persian), Extended abstracts; 3: 7–8.
22.
KarimiJPourtakdoustSHNobahariH. Trim and maneuverability analysis of a UAV using a new constrained PSO approach. J Aerosp Sci and Technol (JAST), Iranian Aerospace Society2011; 8(1): 1–12.
23.
Kennedy J and Eberhart RC. Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Perth, WA, Australia, 27 November–1 December 1995, pp.1942–1948. Piscataway, NJ: IEEE.
24.
EngelbrechtAP. Fundamentals of computational swarm intelligence. Chichester: John Wiley & Sons, Ltd, 2005.
25.
ZavalaAMAguirreAHDiharceEV. Robust PSO-based constrained optimization by perturbing the particle’s memory. In: Swarm intelligence. Focus on ant and particle swarm optimization. Croatia: I-Tech Education and Publishing, 2007, pp.57–76.
26.
Laskari EC, Parapoulos KE and Vrahatis MN. Particle swarm optimization for integer programming. In: Proceedings of the IEEE 2002 congress on evolutionary computation, Hawaii, HI, USA, 12–15 May 2002, pp.1576–1581. Piscataway, NJ: IEEE.
27.
BabaieAKarimiJ. Optimal trajectory planning for a UAV in presence of terrain and threats. Aerosp Mech J Imam Hessein Univ2011; 7(2): 57–69.
28.
RoskamJ. Airplane flight dynamics and automatic flight controls. Lawrence, KS: DAR Corporation, 1998.
