Fuel-Efficient Trajectories Traffic Synchronization

Pawełek, Adrian; Lichota, Piotr; Dalmau, Ramon; Prats, Xavier

doi:10.2514/1.c034730

Cited by 11 publications

(17 citation statements)

References 18 publications

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“…The tracking problem is of great importance in advanced aircraft control [1][2][3][4][5][6][7]. It consists of determining the control for an aircraft that keeps its position close to the prescribed trajectory in some sense.…”

Section: Introductionmentioning

confidence: 99%

Generalized Linear Quadratic Control for a Full Tracking Problem in Aviation

Dul

Lichota

Rusowicz

2020

Sensors

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In this paper, the full tracking problem in aircraft system identification and control is presented. Time domain output error method with maximum likelihood principle was used to perform system identification. The linear quadratic regulator (LQR)-based approach has been used for solving aviation full tracking problems in aviation. It has been shown that the generalized nonlinear LQR control is able to handle such problems even in case of inaccurate measurements and in the presence of moderate disturbances provided that the model of an aircraft is properly identified.

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Section: Introductionmentioning

confidence: 99%

Generalized Linear Quadratic Control for a Full Tracking Problem in Aviation

Dul

Lichota

Rusowicz

2020

Sensors

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“…In the past few years, some research has been conducted on the issue of trajectory optimization. The objectives are to improve traffic efficiency [5,29], minimize fuel consumption [10,27,28,39], minimize aircraft emission and noise impact [12,15,24], minimize the total flying cost [32,42], or their combinations [18]. Some other related issues include the airspace constraints in the optimization model [15], estimation of environmental performance indicators [19], characterization of aircraft emissions [38], trajectory optimization methods [31,32], the environmental and macroeconomic impact of the implementation of energy taxation or emissions trading scheme [1,3,9,11,37], the economic and environmental efficiency of airlines [4,23], etc.…”

Section: Trajectory Optimizationmentioning

confidence: 99%

Spatial–Temporal Clustering and Optimization of Aircraft Descent and Approach Trajectories

Yang

Tang

Chen

et al. 2021

Int. J. Aeronaut. Space Sci.

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This study presents a procedure for the spatial-temporal clustering and optimization of aircraft descent and approach trajectories. First, the spatial-temporal similarity between two trajectories is defined. Clustering analysis are conducted to identify the prevailing trajectories. The clustering centers obtained based on spatial-temporal distance are compared with those obtained based on the traditional Euclidean distance. Second, a multi-objective optimization model is established to minimize fuel consumption, aircraft emission and noise impact considering flight constraints. The Pareto solution that has the highest similarity with the prevailing trajectories is selected as the final optimized trajectory. The performance indicators of the optimized trajectory are compared with the average values of historic trajectories. It is found that travel time, fuel consumption and noise impact for the optimized trajectory are reduced by 5.34%, 0.96% and 11.86%, respectively. The percentages are 0.96%, 1.32%, 9.18%, 3.54% and 4.00% for CO 2 , SO x , NO x , CO and HC, respectively. Also, the performance indicators for the two clustering centers based on spatial-temporal distance are generally closer to average performance of original trajectories, as well as those of the optimized trajectories, as compared with the two clustering centers based on Euclidean distance. The spatial-temporal clustering methods may help to discover the valuable information that lies in those indicators associated with features reflected in time dimension.

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“…where ∆D is the change in drag force. The aircraft point-mass model is commonly used in energy-based methods [24]. Assuming that ∆γ is small, then sin ∆γ = ∆γ; therefore…”

Section: Aircraft Energy Equationsmentioning

confidence: 99%

Experimental Validation of Total Energy Control System for UAVs

et al. 2019

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This paper presents an analysis of a Total Energy Control System (TECS) introduced by Lambregts to control unmanned aerial vehicle (UAV) velocity and altitude by using the total energy distribution. Furthermore, an extended Kalman filter (EKF) approach was used to predict aircraft response in terms of angular rates and linear acceleration during a test flight campaign. From both approaches, state equations were obtained to model the aircraft using Matlab-Simulink. From an aerodynamic study, airplane characteristics were obtained in terms of non-dimensional derivatives and compared to those obtained from the experimental methods. It was determined that TECS approach was very accurate; however, disturbance errors could be decreased by adjusting some model parameters. On the other hand, it was difficult to obtain a real estimation from the EKF method due to the presence of turbulence during flight and the relatively low inertia of the scale model. Dynamic characteristics were validated using a low-cost inertial sensor that cab be easily integrated in UAV platforms. The gathered data can be used to predict model characteristics by integrating the information into flight simulators for future design development.

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Fuel-Efficient Trajectories Traffic Synchronization

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Cited by 11 publications

References 18 publications

Generalized Linear Quadratic Control for a Full Tracking Problem in Aviation

Generalized Linear Quadratic Control for a Full Tracking Problem in Aviation

Spatial–Temporal Clustering and Optimization of Aircraft Descent and Approach Trajectories

Experimental Validation of Total Energy Control System for UAVs

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