Developing a Generalized Trajectory Modeling Framework for Small UAS Performance in the Presence of Wind

Abstract

In the context of UAS Traffic Management operations, where varied vehicle types and uncertain winds are expected, there is a need for a trajectory simulation that trades a vehicle-centric approach with a systems level approach to model expected vehicle performance with an uncertain operational environment. This paper focuses on the first phase of this trajectory prediction development. The goal is to understand the behavior of this new framework as applied to a vehicle targeting a specific terminal altitude. Specifically, a generalized six degree of freedom trajectory model was built to identify vehicle performance in the presence of wind. Generalization of this model was achieved by reducing the number of simplifying assumptions and using vehicle performance parameters, that were non-specific to the control system, to drive the control solution. The inner loop control strategy behind this algorithm is to find the forces and torques required to guide the vehicle to an intended altitude within vehicle-specific force and attitude constraints in the presence of winds. The control solution was optimized via the Artificial Bee Colony genetic optimization method. It was successfully demonstrated that this framework can utilize vehicle performance parameters, that were non-specific to the control system, to optimize a control solution for targeting an operational altitude. Additionally, vertical and lateral path deviations from the nominal case were observed in the wind case results. This forms the basis for quantifying the spatial and temporal requirements a vehicle will need for a given operation.

Rapid uncertainty propagation and chance‐constrained path planning for small unmanned aerial vehicles

“…One of the responsibilities of such a UTM system will likely be to apply a risk-based approach where geographical needs and use cases determine the airspace performance requirements. 4 Addressing this will require a sUAS trajectory prediction model that validates vehicle flight performance and allows for UTM to determine whether or not the vehicle can operate in the airspace given real-time information about wind, other vehicles, and/or obstacles. Given a large number of vehicles predicted to be in operation, this trajectory prediction model must accommodate multiple vehicle types and airspace environments (wind, terrain, etc).…”

Real-time Risk Assessment Framework for Unmanned Aircraft System (UAS) Traffic Management (UTM)

“…One of the objectives of the UTM project is the development of modeling and simulation methods that would allow prediction of flight dynamics behaviors and trajectories suitable for risk analysis as well as implementation in a real-time risk assessment tool. [24][25][26] Due to practical considerations, low-order, generic modeling methods were sought since they offer the potential for rapid solutions and easier implementation.…”

Small Unmanned Aircraft System (sUAS) Trajectory Modeling in Support of UAS Traffic Management (UTM)

“…One example is the dynamics model and controller developed for the Stanford Rotorcraft for Multi-Agent Control (STARMAC) fleet of quadrotor aircraft [13], [14]. More recently, to study sUAS trajectory in the presence of wind, D'Souza modeled multirotor vehicles as a point mass with a simplified set of EOM and a gain scheduled proportional-integralderivative controller (PID) controller [8]. A genetic optimization method was used to optimize the controller.…”

“…The algorithm was simulated with the UAS flying at constant altitude in a uniform wind field to achieve required arrival times. D'Souza et al developed a generalized six-degree-of-freedom (6DoF) multirotor trajectory model to identify vehicle performance in the presence of wind [7], [8]. The generalized model uses performance parameters that were non-specific to the control system to derive the control solution.…”

“…This is different from traditional trajectory prediction methods for air traffic management (ATM) application, which introduce many simplifications. In the latest effort [8], a genetic optimization method was used to optimize the control solution. The algorithm was tested to simulate climb to a given altitude with vehicle force and attitude constraints.…”