A trajectory planning model of tiltrotor considering multi-phase and multi-mode flight

Abstract

A trajectory planning model of tiltrotor with multi-phase and multi-mode flight is proposed in this paper. The model is developed to obtain the trajectory of tiltrotor with consideration of flight mission and environment. In the established model, the flight mission from take-off to landing is composed of several phases which are related to the flight modes. On the basis of the flight phases and the flight modes, the trajectory planning model of tiltrotor is described from three aspects: i.e. tiltrotor dynamics including motion equations and maneuverability, flight mission requirements, and flight environment including different no-fly zones. Then, particle swarm optimization algorithm is applied to generate the trajectory of tiltrotor online. The strategy of receding horizon optimization is adopted, and the control inputs in the next few seconds are optimized by particle swarm optimization algorithm. Flight mission simulations with different situations are carried out to verify the rationality and validity of the proposed trajectory planning model. Simulation results demonstrate that the tiltrotor flying with multi-mode can reach the target in three cases and can avoid both static and dynamic obstacles.

Keywords

Tiltrotor trajectory planning multi-phase flight multi-mode flight artificial potential field

Get full access to this article

View all access options for this article.

References

Wang

. Multi-body motion modeling and simulation for tilt rotor aircraft. Chin J Aeronaut 2010; 23(4): 415–422.

Zhang

. Flight dynamics modeling of tilt rotor aircraft based on Lagrange equation. Flight Dyn 2017; 35(3): 11–15. [Chinese].

Murrieta-Mendoza

Hamy

Botez

. Four- and three-dimensional aircraft reference trajectory inspired by ant colony optimization. J Aerosp Inform Syst 2017; 14(11): 597–616.

Franco

Rivas

. Analysis of optimal aircraft cruise with fixed arrival time including wind effects. Aerosp Sci Technol 2014; 32(1): 212–222.

Sridhar

Grabbe

. optimizing aircraft trajectories with multiple cruise altitudes in the presence of winds. J Aerosp Inform Syst 2014; 11(1): 35–46.

Scherer

Chamberlain

Singh

. Autonomous landing at unprepared sites by a full-scale helicopter. Robot Autonom Syst 2012; 60(12): 1545–1562.

Asadi

Sabzehparvar

Atkins

, et al. Damaged airplane trajectory planning based on flight envelop and motion primitives. J Aircr 2014; 51(6): 1740–1757.

Franco

Rivas

. Optimization of multiphase aircraft trajectories using hybrid optimal control. J Guidance Control Dyn 2015; 38(3): 452–467.

Rivas

Valenzuela

de Augusto

. Computation of global trajectories of commercial transport aircraft. Proc IMechE, Part G: J Aerosp Eng 2013; 227(G1): 142–158.

10.

Zhao

Zhou

. Reentry trajectory optimization for hypersonic vehicle satisfying complex constraints. Chin J Aeronaut 2013; 26(6): 1544–1553.

11.

Zhang

Sun

, et al. Time-varying linear control for tiltrotor aircraft. Chin J Aeronaut 2018; 31(4): 632–642.

12.

Rosenstein H, McVeigh MA and Mollenkof PA. V/STOL tilt rotor aircraft study mathematical model for a real time simulation of a tilt rotor aircraft. Report, NASA, California, 1973. Report No.: CR114601.

13.

Harendra PB, Joglekar MJ and Gaffey TM. Hover low speed and conversion tests of a tilt rotor aeroelastic model. Report, Bell Helicopter Company, Texas, 1973. Report No.: 301-099-002.

14.

Carlson EB, Zhao YY and Chen RTN. Optimal tiltrotor runway operations in one engine inoperative. In: AIAA guidance, navigation, and control conference, 9–11 August 1999, pp. 75–85. Portland, OR: AIAA.

15.

Kufeld

Bousman

. High load conditions measured on a UH‐60A in maneuvering flight. J Am Helicopter Soc 1998; 43(3): 202–211.

16.

Khatib

. Real-time obstacle avoidance for manipulators and mobile robots. Int J Robot Res 1986; 5(1): 90–98.

17.

Stastny

Garcia

Keshmiri

. Collision and obstacle avoidance in unmanned aerial systems using morphing potential field navigation and nonlinear model predictive control. J Dyn Syst Meas Control 2015; 136(1): 014503–014503.

18.

Lin

Aouf

. Potential-field-based evolutionary route planner for the control of multiple unmanned aerial vehicles. Proc IMechE, Part G: J Aerospace Engineering 2010; 224(G11): 1229–1242.

19.

Chen

Jiang

. A modified ant optimization algorithm for path planning of UCAV. Appl Soft Comput 2008; 8(4): 1712–1718.

20.

Zhang

Duan

. Three-dimensional path planning for uninhabited combat aerial vehicle based on predator-prey pigeon-inspired optimization in dynamic environment. IEEE-ACM Trans Comput Biol Bioinform 2017; 14(1): 97–107.

21.

Foo

Knutzon

Kalivarapu

, et al. Path planning of unmanned aerial vehicles using B-splines and particle swarm optimization. J Aerosp Comput Inform Commun 2009; 6(4): 271–290.