Trajectory Simulation With Battery Modeling for Electric Powered Unmanned Aerial Vehicles

Fixed wing electric powered unmanned aerial vehicles (UAVs) has been widely adopted for the last decade in a great number of applications. One of the primary advantages to fixed wing versus multi-rotor designs is the efficiency in forward flight with best possible range and endurance capabilities. In electrically powered air vehicles range and endurance are monitored by the State-of-Charge (SOC) of the battery. To understand the capabilities of the battery, discharge experiments can be conducted in lab environments; however, sometimes the results are difficult to integrate in flight simulations. In this thesis, a trajectory simulation is developed that can estimate an instantaneous SOC and terminal voltage of the Lithium Polymer (Li-Po) battery of a fixed wing UAV. The simulation code is generated using the traditional flight dynamics equations for a mathematical five degree of freedom (5-DOF) system in the MATLAB environment. Simplistic control relations are defined for setting the pitch angle(θ) and roll angle(��) of the UAV. An AVISTAR ELITE RC model has been chosen to simulate the flight mission with the goal of future flight test validations. Initially, battery simulation was carried out in the ODU UAV lab by discharging a 3300Mah Li-Po battery to half capacity with constant current over a range of current draw. Later, these constant current discharge curves were converted to the constant power curves which are more suitable for the battery powered aircraft applications. Simulated battery pulse discharge tests were also conducted, and battery parameters were estimated in SIMULINK for the validation of the constant power method used in the simulation. The overall results of this research demonstrate the endurance and range of the electric UAV for mission paths that include takeoff, climb/descent and turning flight phases

Aircraft dynamics model augmentation for RPAS navigation and guidance

An Aircraft Dynamics Model (ADM) augmentation scheme for Remotely Piloted Aircraft System (RPAS) navigation and guidance is presented. The proposed ADM virtual sensor is employed in the RPAS navigation system to enhance continuity and accuracy of positioning data in case of Global Navigation Satellite System (GNSS) data degradations/losses, and to improve attitude estimation by vision based sensors and Micro-Electromechanical System Inertial Measurement Unit (MEMS-IMU) sensors. The ADM virtual sensor is essentially a knowledge-based module that predicts RPAS flight dynamics (aircraft trajectory and attitude motion) by employing a rigid body 6-Degree of Freedom (6-DoF) model. Two possible schemes are studied for integration of the ADM module in the aircraft navigation system employing an Extended Kalman Filter (EKF) and an Unscented Kalman Filter (UKF). Additionally, the synergy between the navigation systems and an Avionics-Based Integrity Augmentation (ABIA) module is examined and a sensor-switching framework is proposed to maintain the Required Navigation Performance (RNP) in the event of single and multiple sensor degradations. The ADM performance is assessed through simulation of an RPAS in representative fight operations. Sensitivity analysis of the errors caused by perturbations in the input parameters of the aircraft dynamics is performed to demonstrate the robustness of the proposed approach. Results confirm that the ADM virtual sensor provides improved performance in terms of data accuracy/continuity, and an extension of solution validity time, especially when pre-filtered and employed in conjunction with a UKF