General Formulation of Powered Flight Trajectory Optimization Problems

1958 ◽  
Vol 29 (8) ◽  
pp. 1203-1209 ◽  
Author(s):  
Burton D. Fried
Keyword(s):  
Trajectory Optimization ◽  
Optimization Problems ◽  
Flight Trajectory

scholarly journals A Modified Dynamic Programming Approach for 4D Minimum Fuel and Emissions Trajectory Optimization

Aerospace ◽  
2021 ◽  
Vol 8 (5) ◽  
pp. 135
Author(s):  
Kawser Ahmed ◽  
Kouamana Bousson ◽  
Milca de Freitas Coelho
Keyword(s):  
Dynamic Programming ◽  
Fuel Consumption ◽  
Trajectory Optimization ◽  
Optimization Problems ◽  
Computational Effort ◽  
Programming Approach ◽  
Optimal Trajectories ◽  
Flight Trajectory ◽  
Aircraft Fuel ◽  
Fuel Consumption And Emissions

4D flight trajectory optimization is an essential component to improve flight efficiency and to enhance air traffic capacity. this technique not only helps to reduce the operational costs, but also helps to reduce the environmental impact caused by the airliners. This study considers Dynamic Programming (DP), a well-established numerical method ideally suited to solve 4D flight Trajectory Optimization Problems (TOPs). However, it bears some shortcomings that prevent the use of DP in many practical real-time implementations. This paper proposes a Modified Dynamic Programming (MDP) approach that reduces the computational effort and overcomes the drawbacks of the traditional DP. In this paper, two numerical examples with fixed arrival times are presented, where the proposed MDP approach is successfully implemented to generate optimal trajectories that minimize aircraft fuel consumption and emissions. Then the obtained optimal trajectories are compared with the corresponding reference commercial flight trajectory for the same route in order to quantify the potential benefit of reduction of aircraft fuel consumption and emissions.

scholarly journals Data Downlink System in the Vast IOT Node Condition Assisted by UAV, Large Intelligent Surface, and Power and Data Beacon

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5748
Author(s):  
Zhibo Zhang ◽  
Qing Chang ◽  
Na Zhao ◽  
Chen Li ◽  
Tianrun Li
Keyword(s):  
Communication Systems ◽  
Optimization Problems ◽  
Minimum Energy ◽  
Numerical Algorithms ◽  
Flight Trajectory ◽  
Performance Improvements ◽  
Minimum Energy Consumption ◽  
Significant Performance ◽  
Downlink System ◽  
The Internet Of Things

The future development of communication systems will create a great demand for the internet of things (IOT), where the overall control of all IOT nodes will become an important problem. Considering the essential issues of miniaturization and energy conservation, in this study, a new data downlink system is designed in which all IOT nodes harvest energy first and then receive data. To avoid the unsolvable problem of pre-locating all positions of vast IOT nodes, a device called the power and data beacon (PDB) is proposed. This acts as a relay station for energy and data. In addition, we model future scenes in which a communication system is assisted by unmanned aerial vehicles (UAVs), large intelligent surfaces (LISs), and PDBs. In this paper, we propose and solve the problem of determining the optimal flight trajectory to reach the minimum energy consumption or minimum time consumption. Four future feasible scenes are analyzed and then the optimization problems are solved based on numerical algorithms. Simulation results show that there are significant performance improvements in energy/time with the deployment of LISs and reasonable UAV trajectory planning.

Flight Trajectory Optimization of Sailplane After Rope Break

2021 ◽  
pp. 1-13
Author(s):  
Armando R. Collazo Garcia ◽  
Prateek Ranjan ◽  
Kevin J. Chen ◽  
Kai A. James ◽  
Phillip J. Ansell
Keyword(s):  
Trajectory Optimization ◽  
Flight Trajectory

scholarly journals A trajectory design method for RLV via artificialmemory-principle optimization

2018 ◽  
Vol 189 ◽  
pp. 10019
Author(s):  
Hao Li ◽  
Changzhu Wei
Keyword(s):  
Trajectory Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Memory Cell ◽  
Optimization Method ◽  
Alternative Solution ◽  
Trajectory Design ◽  
Memory State ◽  
Artificial Memory

A trajectory optimization method for RLV based on artificial memory principles is proposed. Firstly the optimization problem is modelled in Euclidean space. Then in order to solve the complicated optimization problem of RLV in entry phase, Artificial-memory-principle optimization (AMPO) is introduced. AMPO is inspired by memory principles, in which a memory cell consists the whole information of an alternative solution. The information includes solution state and memory state. The former is an evolutional alternative solution, the latter indicates the state type of memory cell: temporary, short-and long-term. In the evolution of optimization, AMPO makes a various search (stimulus) to ensure adaptability, if the stimulus is good, memory state will turn temporary to short-term, even long-term, otherwise it not. Finally, simulation of different methods is carried out respectively. Results show that the method based on AMPO has better performance and high convergence speed when solving complicated optimization problems of RLV.

DirCol5i: Trajectory Optimization for Problems With High-Order Derivatives

2018 ◽  
Vol 141 (3) ◽  
Author(s):  
Matthew P. Kelly
Keyword(s):  
Nonlinear System ◽  
Trajectory Optimization ◽  
Optimization Problems ◽  
High Order ◽  
Robot Arm ◽  
Minimum Jerk ◽  
Higher Derivatives ◽  
A Chain ◽  
Fifth Order ◽  
Reference Trajectories

In this technical brief, we focus on solving trajectory optimization problems that have nonlinear system dynamics and that include high-order derivatives in the objective function. This type of problem comes up in robotics—for example, when computing minimum-snap reference trajectories for a quadrotor or computing minimum-jerk trajectories for a robot arm. DirCol5i is a transcription method that is specialized for solving this type of problem. It uses the fifth-order splines and analytic differentiation to compute higher-derivatives, rather than using a chain-integrator as would be required by traditional methods. We compare DirCol5i to traditional transcription methods. Although it is slower for some simple optimization problems, when solving problems with high-order derivatives DirCol5i is faster, more numerically robust, and does not require setting up a chain integrator.

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