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Trajectory feasibility evaluation using path prescribed control of unmanned aerial vehicle in differential algebraic equations framework

Uppal, T and Raha, S and Srivastava, S (2017) Trajectory feasibility evaluation using path prescribed control of unmanned aerial vehicle in differential algebraic equations framework. In: AERONAUTICAL JOURNAL, 121 (1240). pp. 770-789.

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Official URL: http://doi.org/10.1017/aer.2017.36


Mission simulation is a critical activity in the development and operation of Unmanned Aerial Vehicles (UAVs). It is important to ascertain the feasibility of a trajectory in a mission. In this work, an algorithm has been developed for feasibility study of a trajectory of a UAV using prescribed path optimal control through an inverse simulation method. This has been done under a Differential Algebraic Equations (DAE)/Inequalities (DAI) framework. The UAV model together with constraints is represented as a high index DAE system. The trajectory that UAV shall take is prescribed as one of the constraint equations. The solution for the DAE system is obtained using a variation of the alpha method that is capable of handling both equality and inequality constraints on system dynamics. The algorithm involves direct numerical integration of a DAI formulation in a time-stepping manner using a Sequential Quadratic Programming (SQP) solver that detects and satisfy active path constraints at each time step (mesh point). In this unique approach, the model and the constraints are always solved together. The method ensures stable solution at each time step, local minimum at each iteration of simulation and provides a regularised basis to the solver. A typical UAV trajectory has been simulated and demonstrated in this paper. This new approach can be used for path planning of UAVs before the actual control law is designed for flight control computer. Compared to other existing computationally intensive techniques, this approach is computationally simple, ensures continuous constraint satisfaction and provides a viable option for model predictive control of UAVs.

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the CAMBRIDGE UNIV PRESS, EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 07 Dec 2017 09:37
Last Modified: 15 Oct 2018 08:07
URI: http://eprints.iisc.ac.in/id/eprint/58402

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