Reconfigurable Mission Plans for RPAS

Usach, Hector; Vila, J.

doi:10.1016/j.ast.2019.105528

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…To find the solution (shown in Figure 5) for ailerons, elevator parts and flaps (signal values for each interval) a genetic algorithm was used due to the large search space size (3 20 possible solutions) and multiple local minima. The algorithm scheme is presented in Figure 6.…”

Section: Multi-step Inputsmentioning

confidence: 99%

“…The primary aim of the reconfiguration is to eliminate effects of the system failures; however, it may also be used in order to gain tactical advantage during air combat or for investigations performed in in-flight simulators. The reconfiguration is performed either by using flight controls that are not used in the particular flight phase [13,14], by modifying control laws [15][16][17][18] or by readjusting the mission plan [19,20]. In each of those approaches a valid mathematical model is required.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Axis Inputs for Identification of a Reconfigurable Fixed-Wing UAV

Lichota¹

2020

Aerospace

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Designing a reconfiguration system for an aircraft requires a good mathematical model of the object. An accurate model describing the aircraft dynamics can be obtained from system identification. In this case, special maneuvers for parameter estimation must be designed, as the reconfiguration algorithm may require to use flight controls separately, even if they usually work in pairs. The simultaneous multi-axis multi-step input design for reconfigurable fixed-wing aircraft system identification is presented in this paper. D-optimality criterion and genetic algorithm were used to design the flight controls deflections. The aircraft model was excited with those inputs and its outputs were recorded. These data were used to estimate stability and control derivatives by using the maximum likelihood principle. Visual match between registered and identified outputs as well as relative standard deviations were used to validate the outcomes. The system was also excited with simultaneous multisine inputs and its stability and control derivatives were estimated with the same approach as earlier in order to assess the multi-step design.

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Section: Multi-step Inputsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Axis Inputs for Identification of a Reconfigurable Fixed-Wing UAV

Lichota¹

2020

Aerospace

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“…With the continuous development of technology, people have conducted research on mission planning problems at different levels. Both mission planning and autonomous task planning have been explored in depth and widely applied in various fields, such as aircraft mission planning, ground robot task planning, and ground unmanned combat command system task planning [5][6][7][8][9]. Compared with a simple planning problem, UUV mission planning research started recently, and it can learn from the more mature technology research in the fields of robots, aircraft, satellites, and so on.…”

Section: Introductionmentioning

confidence: 99%

Man-in-the-Loop Control and Mission Planning for Unmanned Underwater Vehicles

Han,

Wang,

Yuan

et al. 2024

JMSE

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UUVs (unmanned underwater vehicles) perform tasks in the marine environment under direction from a commander through a mother ship control system. In cases where communication is available, a UUV task re-planning system was designed to ensure task completion despite uncertain events faced by UUVs. First, the XML language standardizes the expression of UUV task elements. Second, considering the time sequence and spatial path planning requirements of human-supervised UUV control tasks, time sequence planning based on a genetic algorithm and spatial path planning based on an improved genetic algorithm were designed to plan near-optimal approximate spatial paths for control tasks. Third, uncertainties encountered during UUV task execution were classified so that the commander could adjust according to the situation or invoke the control task re-planning algorithm to re-plan. Finally, a simulation platform was built using the QT development environment to simulate human-supervised UUV control task planning and re-planning, verifying the algorithm’s design effectiveness.

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