Aircraft terrain following flights based on fuzzy logic

Rahim, Mohammad Akmal Baharain Abdul; Malaek, Seyed Móhammad‐Bagher

doi:10.1108/00022661111120980

Cited by 14 publications

(5 citation statements)

References 21 publications

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“…The o ine paths were used to train a multi-layer neural network. In Rahim and Malaek [28,29], an optimal trajectory planning plus a fuzzy approach was proposed for TF/TA ight maneuvers. In this method, the relationship between the gradient of terrain slopes and their derivatives, as well as the ight speed and altitude of the aircraft, was used to construct fuzzy rules.…”

Section: Arti Cial Intelligence (Ai) Approachesmentioning

confidence: 99%

TF/TA Optimal Flight Trajectory Planning Using a Novel Regenerative Flattener Mapping Method

Kosari

Kassaei

2019

Scientia Iranica

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In this paper, a new methodology is proposed to enhance the conformal mapping applications in the process of optimum trajectory planning in Terrain Following (TF) and Terrain Avoidance (TA) Flights. The new approach uses the conformal mapping concept as a attener tool to transform the constrained trajectory-planning problem with ight altitude restrictions due to the presence of obstacles into a regenerated problem with no obstacle and minimal height constraints. In this regard, the Schwarz-Christo el theorem was utilized to incorporate the height constraints into the aircraft dynamic equations of motion. The regenerated optimal control problem was then solved by a numerical method, namely the direct Legendre-Gauss-Radau pseudospectral algorithm. A composite performance index of ight time, terrain masking, and aerodynamic control e ort was optimized. Furthermore, to obtain realistic trajectories, the aircraft maximum climb and descent rates were imposed as inequality constraints in the solution algorithm. Several case studies for two-dimensional ight scenarios show the applicability of this approach in TF/TA trajectory planning. Extensive simulations con rm the e ciency of the proposed approach and verify the feasibility of solutions, satisfying all of the constraints underlying the problem.

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Section: Arti Cial Intelligence (Ai) Approachesmentioning

confidence: 99%

TF/TA Optimal Flight Trajectory Planning Using a Novel Regenerative Flattener Mapping Method

Kosari

Kassaei

2019

Scientia Iranica

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“…The use of fuzzy logic in air traffic control allows the uncertainty of the information inherent in this type of problem to be considered in a systematic way. Takeoff and landing [23], flight level changes and speed control [24], [25], altitude control [26], the setting of flight routes ( [27], [28]) and conflict detection and resolution [29] are examples of tasks that are well suited to the use of fuzzy logic (types I and II).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Detecting and Resolving Conflicts in Air Traffic Routes

et al. 2019

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This paper presents a hybrid model for the detection and resolution of conflicts in air traffic routes involving flight level change actions and adjustment of the longitudinal acceleration of aircraft. The strategy comprises an integrated approach that uses a fuzzy model to quantify the level of longitudinal conflict between two aircraft on the same airway. In addition, optimum flight level change actions between aircraft are calculated through a global and dynamic analysis involving the recognition of clusters of aircraft in conflict and the search for the best scenario by means of a genetic algorithm that minimizes the sum of positive conflicts. The results show that the proposed approach is able to detect and remove longitudinal conflicts in advance, providing a potential tool to support decision-making, improve safety and optimize the use of airspace.

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“…Rahim and Malaek 8,9 perform optimal path planning for TF/TA maneuvers using fuzzy logic method for ground robots, submarines, and unmanned flying vehicles. In that work, to simulate the TF maneuver, terrain with various gradients has been used and the flight altitude and gradient of the terrain have been modeled with the fuzzy method.…”

Section: Introductionmentioning

confidence: 99%

“…In that work, to simulate the TF maneuver, terrain with various gradients has been used and the flight altitude and gradient of the terrain have been modeled with the fuzzy method. In Rahim and Malaek, 9 the implementation of fuzzy method was done in two phases. The first phase includes offline navigation in which path planning is done through cost function, flight vehicle dynamics, and terrain data with the fuzzy method.…”

Section: Introductionmentioning

confidence: 99%

Path generation for flying robots in mountainous regions

Kosari

Maghsoudi

Lavaei

2016

International Journal of Micro Air Vehicles

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In this paper, a new path planning method is proposed to resolve the problem of two-dimensional terrain following flight of flying robots in mountainous regions. The performance criteria considered for this mission design could include either the minimum vertical acceleration or the minimum flying time. To impose the terrain following/terrain avoidance constraints, various approaches such as least square method, Fourier series method, Gaussian estimation method, and Chebyshev orthogonal polynomial are explored. The resulting optimal control problem is discretized by employing a numerical technique namely direct collocation and then transformed into a nonlinear programming problem. The efficacy of the proposed method is demonstrated by extensive simulations, and particularly, it has been verified that this method is able to produce a solution that satisfies all hard constraints of the underlying problem.

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Aircraft terrain following flights based on fuzzy logic

Cited by 14 publications

References 21 publications

TF/TA Optimal Flight Trajectory Planning Using a Novel Regenerative Flattener Mapping Method

TF/TA Optimal Flight Trajectory Planning Using a Novel Regenerative Flattener Mapping Method

A Hybrid Approach for Detecting and Resolving Conflicts in Air Traffic Routes

Path generation for flying robots in mountainous regions

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