Trajectory optimization algorithm for a constant altitude cruise flight with a required time of arrival constraint

2015 ◽  
Author(s):  
Alexandre Liv ◽  
Radu Dancila ◽  
Ruxandra M. Botez
Keyword(s):  
Optimization Algorithm ◽  
Trajectory Optimization ◽  
Time Of Arrival ◽  
Constant Altitude ◽  
Cruise Flight ◽  
Required Time Of Arrival

Vertical flight profile optimization for a cruise segment with RTA constraints

2019 ◽  
Vol 123 (1265) ◽  
pp. 970-992
Author(s):  
R. Dancila ◽  
R. Botez
Keyword(s):  
Performance Model ◽  
Time Of Arrival ◽  
Flight Performance ◽  
Flight Trajectory ◽  
Flight Profile ◽  
Profile Optimization ◽  
Wind Profiles ◽  
Cruise Flight ◽  
Active Controls ◽  
Required Time Of Arrival

ABSTRACTThis paper presents the results of a research performed at the Research Laboratory in Active Controls, Avionics and Aeroservoelasticty (LARCASE), at ÉTS, concerning optimisation strategies for cruise flight segments with imposed flight time (delimited by waypoints with required time of arrival constraints). Specifically, a new algorithm is presented that identifies the optimal vertical navigation profile (flight altitude and speed optimisation) for a cruise segment with imposed lateral navigation profile, bounded by two waypoints with required time of arrival constraints. The set of evaluated vertical navigation profiles are characterised by identical altitudes and speeds at their initial and final waypoints (at the beginning and the end of the cruise segment under optimisation), a maximum of one altitude step (relative to the initial altitude), and are flown at constant speed. This study investigates the flight performance increase (total cost reduction) for a flight along the optimal vertical navigation profile, relative to a flight at the optimal speed and initial cruise altitude. The evaluation was performed using a medium haul transport aircraft flight performance model, for three lateral navigation profiles and three wind profiles. The algorithm is targeted for Flight Management Systems platforms, to provide the optimal flight trajectory for the imposed lateral flight profile and time constraints.

Data-Driven Method for the Prediction of Estimated Time of Arrival

2021 ◽  
pp. 036119812110332
Author(s):  
Xuhao Gui ◽  
Junfeng Zhang ◽  
Zihan Peng ◽  
Chunwei Yang
Keyword(s):  
Pattern Classification ◽  
Trajectory Optimization ◽  
Time Estimation ◽  
Flight Time ◽  
Classification Model ◽  
Time Of Arrival ◽  
Trajectory Clustering ◽  
Clustering Method ◽  
Arrival Pattern ◽  
Multiple Stages

Predicting the estimated time of arrival (ETA) plays an essential role in decision support (conflict detection, arrival sequencing, or trajectory optimization) for air traffic controllers. In this paper, a new multiple stages strategy for ETA prediction is proposed based on radar trajectories, including arrival pattern identification, arrival pattern classification, and flight time estimation. First, an intention-oriented trajectory clustering method is developed based on a new trajectory representation technique. Such a proposed trajectory clustering method can group trajectories into different arrival patterns in an efficient way. Second, an arrival pattern classification model is constructed based on random forest and XGBoost algorithms. Then, a flight time regression model is trained for each arrival pattern by using the XGBoost algorithm. Information on current states, historical states, and traffic situations is considered to build the feature set during these processes. Finally, the arrival operation toward Guangzhou International Airport is chosen as a case study. The results illustrate that the proposed method and feature engineering approach could improve the performance of ETA prediction. The proposed multiple stages strategy is superior to the single-model-based ETA prediction.

scholarly journals NLOS Error Mitigation in Cellular Positioning using PSO Optimization Algorithm

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Stevo Lukić ◽  
Mirjana Simić
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Mobile Station ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Cellular Radio ◽  
Object Function ◽  
Swarm Optimization ◽  
Station Location

Non-Line-Of-Sight conditions pose a major challenge to cellular radio positioning. Such conditions, when the direct Line-Of-Sight path is blocked, result in additional propagation delay for the signal, additional attenuation, and an angular bias. Therefore,many researchers have proposed various algorithms to mitigate the measured error caused by this phenomenon. This paper presentsthe procedure for improving accuracy of determining the mobile station location in cellular radio networks in Non-Line-of-Sightpropagation environment, based on the Time Of Arrival oriented estimator using the Particle Swarm Optimization algorithm. Incomputer science, Particle Swarm Optimization is an evolutionary computational method that optimizes a problem by iteratively tryingto improve a candidate solution with regard to a given measure of quality. The proposed algorithm uses the repeating Time-Of-Arrivaltest measurements using the four base stations and for simulation selects the measurement combination that give the smallest regionenclosed by the overlap of four circles. In this way, the smallest intersect area of the four Time-Of-Arrival circles is obtained, andtherefore the smallest positioning error. After that, we consider the complete problem as a combinatorial optimization problem withthe corresponding object function that represents the nonlinear relationship between the intersection of the four circles and the mobilestation location. The Particle Swarm Optimization finds the optimal solution of the object function and efficiently determines themobile station location. The simulation results show that the proposed method outperforms conventional algorithms such as theWeighted Least Squares and the Levenberq-Marquardt method.

A general trajectory optimization method for aircraft taxiing on flight deck of carrier

2018 ◽  
Vol 233 (4) ◽  
pp. 1340-1353 ◽  
Author(s):  
Yu Wu ◽  
Ning Hu ◽  
Xiangju Qu
Keyword(s):  
Optimization Algorithm ◽  
Trajectory Optimization ◽  
Fitness Function ◽  
Control Variable ◽  
Optimization Method ◽  
Operation Efficiency ◽  
Ground Motion Model ◽  
Aircraft System ◽  
Chicken Swarm Optimization ◽  
Flight Deck

Enhancing operation efficiency of flight deck has become a hotspot because it has an important impact on the fighting capacity of the carrier–aircraft system. To improve the operation efficiency, aircraft need taxi to the destination on deck with the optimal trajectory. In this paper, a general method is proposed to solve the trajectory optimization problem for aircraft taxiing on flight deck considering that the existing methods can only deal with the problem in some specific cases. Firstly, the ground motion model of aircraft, the collision detection strategy and the constraints are included in the mathematical model. Then the principles of the chicken swarm optimization algorithm and the generality of the proposed method are explained. In the trajectory optimization algorithm, several strategies, i.e. generation of collocation points, transformation of control variable, and setting of segmented fitness function, are developed to meet the terminal constraints easier and make the search efficient. Three groups of experiments with different environments are conducted. Aircraft with different initial states can reach the targets with the minimum taxiing time, and the taxiing trajectories meet all the constraints. The reason why the general trajectory optimization method is validated in all kinds of situations is also explained.

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