The 4DT planner and Tracker optimiser blocks are illustrated in Figure 2. Generator 2. Path Correction — It corrects the path deviation in Optimiser Monitor terms of lateral, vertical and time profiles and the generated steering commands are provided to the guidance module of the NG-MMS.
In case a warning flag is generated, a recapture command is used to trigger the 4DT regeneration and optimisation process. The ground inter- ground command, control and intelligence system communication system consists of a ground-to- aided by LOS and BLOS communication links.
The TDA loop consists of the following groups of single trajectories t belonging to the global functions: set T. The consecutive Trajectory Change Points data from the environment. The is tracked, suitable decision logics are employed NG-MMS trajectory optimisation algorithms are for identifying the possibility of collisions.
The 3-DoF then the on-board computers determine an action Equations of Motion EoM describing the aircraft to avoid the collision by re-generating the 4DT states and governing the translational movements and optimising it against the set constraints and along the longitudinal, lateral and vertical axes are: performance parameters.
The bank angle is incorporate three control variables determined based on RPAS dynamics and airspace where is the engine power setting, is configurations. In order to construct the vertical the load factor and is the bank angle. These form profile, a number of energy balance equations are the inputs of the dynamic system. The is the geodetic longitude, is the altitude, is the integration steps are constrained by the mission true air speed, is the flight path angle, is the profile imposed altitude, speed and time restrictions heading.
The data that drives the energy balance the nominal acceleration due to gravity of the Earth. Wind effects are considered 16 along the three translational axes of the 3-DoF motion equations. The geodetic coordinate reference where is the true air speed, is the gross system used is the World Geodetic System of year weight. The optimisation problem of of arrival target defined by the 4-PNV system, which determining the states x t depends on the becomes the Required Time of Arrival RTA to be performance index, denoted by a sum of Mayer used by the NG-MMS in determining the optimal and Lagrange terms and it is given by: trajectory states final time.
In general, all flight phases where is the initial time and is the final time in allow for incorporating a cost index based on a the time epoch, t. The time 10 cost, ti is given by: The path constraints are expressed as: ti tt 17 11 Fuel consumption optimisation is achieved by The boundary conditions describing the initial and minimising the difference between the aircraft initial final states are given by: and final mass, which is included as the it DAE of the 3-DoF.
Fuel consumption optimisation is 12 achieved by minimising the difference between the The lateral path is constructed in terms of segments aircraft initial and final mass: straight and turns and is generally based on the l i t - i t 18 required course change and the aircraft predicted ground speed during the turn. A turn is constructed In terms of aircraft emissions, although engine based on the maximum ground speed of the RPAS design and other factors play an influence on the during a course change and the turn radius given by: total amount of emission release, engine emission is generally considered as a function of fuel burn, 13 multiplied by a direct emission factor,.
Hence, the mathematical description of the emission rate, where is the maximum RPAS ground speed issio defined with respect to emissions, e and during the turn. Methods Citations.
Figures from this paper. Citation Type. Has PDF. Publication Type. More Filters. View 2 excerpts. Intelligent Computing and Innovation on Data Science. View 1 excerpt, cites background. Computer Science, Engineering. Figure 4 Research Question and Hypothesis Research Question Q1: Does the degree of complexity in a notional system definition affect the correlation between the functional architecture required to satisfy that system definition as understood separately by human factors and technologist practitioners.
These systems will be based on two battlespace deployments of UAS, one deployed on a ship for use in maritime missions and one to operate in conjunction with ground forces. These definitions will be modified to represent the controlled levels of complexity, and will form the basis for the design efforts which are being monitored.
Once the groups of participants are organized and trained, the design efforts will be executed. The Systems engineering and CSE teams will each have a domain appropriate toolset for capturing the graphical models of their design, and this same toolset will be used by teams of the same type throughout. The models will be validated, and extraneous concepts deleted from the model repositories. The final models will be compared for horizontal, vertical and semantic compatibility on each level of complexity.
Final results will be analyzed and recorded. This toolset will be used to capture a functional model of the system definition using activity modeling. Each team will be provided access to a different UAS subject matter expert who can answer technical questions. They will be located in a laboratory or classroom environment for the duration of the design exercise. Each design excursion will require about 8 hours of work.
Data reduction tools will include SPSS to perform the analyses, the DL algorithms used to validate the model, and a separately prepared test instrument to perform text searches on the model components to determine the values of the Dependent Variables DV described in a follow-on section.
The participants should be technically competent but not highly experienced in development of complex systems. Graduate Students who have completed pre-requisite classes in systems engineering and human factors analysis would be candidates. Each team type will be split into two groups. The group size should be even, with at least three members. The groups will be separated to achieve a relatively uniform average and spread for experience level.
For counter balancing, each group will receive different ordering of the complexity levels. The scenarios are defined in the maritime and ground domains. The system definitions will be created by a joint team of HF and system engineering experts, to include opportunities for approximately leaf node functions and at least three levels of decomposition.
The closed system shall have none of the attributes listed in table 1. The open system will include desired capabilities that present all of the features included in table 1. The scenario presentations for each participant group are summarized in table 2. The testing of the second hypothesis, looking at the relationship between the agreement of models developed by the teams as the complexity level grows, is a within groups analysis.
The design is full factorial. Not every group will see every combination of battlespace domain and complexity level, but battlespace domain was varied to minimize training and boredom residual effects, it is not a control variable. Variables and Data Collection The Independent Variable IV for the experiment is the complexity of the system definition that is to me modeled.
It has three levels, as described above, closed, automated and open. Two teams are used to establish a baseline of natural variation in approach of teams of the same type i. For the level comparison, agreement with either teams concepts will be considered in-level agreement. This is to evaluate whether the variability in agreement due to level exceeds the natural variability between teams of the same competency. Further studies might examine the effects of other components of complexity individually, but for this study, that was considered infeasible.
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