Lunar soft landing trajectory optimization methods

Author(s):  
Zhiyuan Li ◽  
Hongjue Li
2014 ◽  
Vol 721 ◽  
pp. 446-449
Author(s):  
Mo Feng Qu

Based on research carried out for the most fuel-lunar soft landing trajectory optimization problem. First, by improving the function approximation method, the lunar soft landing trajectory optimization problem into a parameter optimization problem, and the optimization variables and state variables have a clear physical meaning. Then use the decimal ant colony algorithm adds local search strategy to study the optimization problem. Finally, the optimization algorithm to optimize term direction angle simulation and error analysis.


2017 ◽  
Vol 60 (9) ◽  
pp. 2060-2076 ◽  
Author(s):  
Huiping Chu ◽  
Lin Ma ◽  
Kexin Wang ◽  
Zhijiang Shao ◽  
Zhengyu Song

Author(s):  
Alejandro Murrieta-Mendoza ◽  
Ruxandra Botez

Vertical Navigation (VNAV) trajectory optimization has been identified as a means to reduce fuel consumption. Due to the computing power limitations of devices such as Flight Management Systems (FMSs), it is very desirable to implement a fast method for calculating trajectory cost using optimization algorithms. Conventional trajectory optimization methods solve a set of differential equations called the aircraft equations of motions to find the optimal flight profile. Many FMSs do not use these equations, but rather a set of lookup tables with experimental, or pre-calculated data, called a Performance Database (PDB). This paper proposes a method to calculate a full trajectory flight cost using a PDB. The trajectory to be calculated is composed of climb, acceleration, cruise, descent and deceleration flight phases. The influence of the crossover altitude during climb and step climbs in cruise were considered for these calculations. Since the PDB is a set of discrete data, Lagrange linear interpolations were performed within the PDB to calculate the required values. Given a takeoff weight, the initial and final coordinates and the desired flight plan, the trajectory model provides the Top of Climb coordinates, the Top of Descent coordinates, the fuel burned and the flight time needed to follow the given flight plan. The accuracy of the trajectory costs calculated with the proposed method was validated for two aircraft; one with an aerodynamic model in FlightSIM, software developed by Presagis, and the other using the trajectory generated by the reference FMS.


2014 ◽  
Vol 59 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Bérénice Mettler ◽  
Zhaodan Kong ◽  
Chad Goerzen ◽  
Matthew Whalley

This paper describes a framework for performance evaluation of autonomous guidance systems. The elements of the framework consist of a set of spatial geometries, flight tasks, performance metrics, a flightdynamic model, and baseline solutions. The spatial benchmarks consist of six tasks in simple geometrical environments and 10 tasks in more complex urban environments based on a real digital terrain elevation map. The framework also includes a set of performance metrics used to compare trajectories. The performance baselines used in the proposed framework are near-optimal solutions computed using one of two trajectory optimization methods: numerical optimization based on nonlinear programming for the simple geometric environments and a motion primitive automaton for problems involving the urban environments. The paper concludes with a demonstration of the benchmarking framework using the Obstacle Field Navigation system developed by the Army Aeroflightdynamics Directorate.


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