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The purpose of this paper is to develop and implement a general sensor model under normal and abnormal operational conditions including nine functional categories (FCs) to provide additional tools for the design, testing and evaluation of unmanned aerial systems within the West Virginia University unmanned air systems (UAS) simulation environment.

The characteristics under normal and abnormal operation of various types of sensors typically used for UAS control are classified within nine FCs. A general and comprehensive framework for sensor modeling is defined as a sequential alteration of the exact value of the measurand corresponding to each FC. Simple mathematical and logical algorithms are used in this process. Each FC is characterized by several parameters, which may be maintained constant or may vary during simulation. The user has maximum flexibility in selecting values for the parameters within and outside sensor design ranges. These values can be set to change at pre-defined moments, such that permanent and intermittent scenarios can be simulated. Sensor outputs are integrated with the autonomous flight simulation allowing for evaluation and analysis of control laws.

The developed sensor model can provide the desirable levels of realism necessary for assessing UAS behavior and dynamic response under sensor failure conditions, as well as evaluating the performance of autonomous flight control laws.

Due to its generality and flexibility, the proposed sensor model allows detailed insight into the dynamic implications of sensor functionality on the performance of control algorithms. It may open new directions for investigating the synergistic interactions between sensors and control systems and lead to improvements in both areas.

The implementation of the proposed sensor model provides a valuable and flexible simulation tool that can support system design for safety purposes. Specifically, it can address directly the analysis and design of fault tolerant flight control laws for autonomous UASs. The proposed model can be easily customized to be used for different complex dynamic systems.

In this paper, information on sensor functionality is fused and organized to develop a general and comprehensive framework for sensor modeling at normal and abnormal operational conditions. The implementation of the proposed approach enhances significantly the capability of the UAS simulation environment to address important issues related to the design of control laws with high performance and desirable robustness for safety purposes.

The purpose of this paper is to develop a simplified atmospheric model including constant wind, turbulence, gusts, and wind shear to provide simulation tools for unmanned aerial vehicle (UAV) design, testing, and evaluation within the West Virginia University (WVU) UAV simulation environment.

Analytical methods and experimental data are used to develop the simplified model for air mass motion as a superposition of four major components. Spatial gradients of relative air velocity vector projections are considered for modeling wind shear effects. The total contribution to relative air velocity from the four components in vehicle body axes is used within the WVU UAV simulation environment to calculate aerodynamic forces and moments. The simplified wind model is also interfaced with aircraft sub-system upset conditions models and different autonomous flight scenarios.

The simplified wind model developed provides simulation of different upset environment flight conditions with desirable levels of realism. It allows the testing, comparison, and evaluation of different trajectory tracking solutions for autonomous flight.

The proposed simplified wind model facilitates the investigation of the effects of different atmospheric scenarios on the performance of trajectory generation algorithms and trajectory tracking control laws.

The proposed simplified wind model has been proved to be a high flexibility tool for simulation of UAVs under normal and abnormal flight conditions. It is expected to provide valuable support for the design and analysis of autonomous flight control laws.

This research effort provides a new capability for the advanced simulation of UAV autonomous flight with practically no additional computational cost. It adds an unprecedented level of detail and versatility to the UAV simulation toolkit within a very user-friendly framework aimed at supporting UAV design and analysis for optimal performance and safety under normal and abnormal flight conditions.

A simplified global positioning system (GPS) error model including models for a variety of abnormal operational conditions and failures is developed to provide simulation tools for the design, testing, and evaluation of autonomous flight fault tolerant control laws. The paper aims to discuss these issues.

Analysis and experimental data are used to build simplified models for GPS position and velocity errors on all three channels. The GPS model is interfaced with West Virginia University unmanned aerial vehicles (UAV) simulation environment and its utility demonstrated through simulation for several autonomous flight scenarios including GPS abnormal operation.

The proposed simplified GPS model achieves desirable levels of accuracy and realism for all components for the purpose of general UAV dynamic simulation and development of fault tolerant autonomous flight control laws.

The simplified GPS model allows investigating GPS malfunction effects on the performance of autonomous UAVs and designing trajectory tracking algorithms with advanced fault tolerant capabilities.

The simplified GPS model has proved to be a flexible and useful tool for UAV simulation and design of autonomous flight control laws at normal and abnormal conditions.

The outcomes of this research effort achieve a level of detail never attempted before in modeling GPS operation at normal and abnormal conditions for UAV simulation and autonomous flight control laws design using a simplified framework.