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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.

An artificial immune system (AIS) for the detection and identification of abnormal operational conditions affecting an unmanned air vehicle (UAV) is developed using the partition of the universe approach. The performance of the proposed methodology is assessed through simulation within the West Virginia University (WVU) unmanned aerial system (UAS) simulation environment.

An AIS is designed and generated for a fixed wing UAV using data from the WVU UAS simulator. A novel partition of the universe approach augmented with the hierarchical multiself strategy is used to define the self, within the AIS paradigm. Several 2-dimensional and 3-dimensional commanded trajectories are simulated under normal and abnormal conditions affecting actuators and sensors. Data recorded are used to build the AIS and develop an abnormal condition detection and identification scheme for the two categories of subsystems. The performance of the methodology is evaluated in terms of detection and identification rates, false alarms and decision times.

The proposed methodology for UAV abnormal condition detection and identification has the potential to support a comprehensive and integrated solution to the problem of aircraft subsystem health management. The novel partition of the universe approach has been proven to be a promising alternative to the previously investigated clustering methods by providing similar or better performance for the cases investigated.

The promising results obtained within this research effort motivate further investigation and extension of the proposed methodology toward a complete system health management process, including abnormal condition evaluation and accommodation.

The use of the partition of the universe approach for AIS generation may potentially represent a valuable alternative to current clustering methods within the AIS paradigm. It can facilitate a simpler and faster implementation of abnormal condition detection and identification schemes.

In this paper, a novel method for AIS generation, the partition of the universe approach, is formulated and applied for the first time for the development of abnormal condition detection and identification schemes for UAVs. This approach is computationally less expensive and mitigates some of the issues related to the typical clustering approaches. The implementation of the proposed approach can potentially enhance the robustness of UAS for safety purposes.

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 analyze and compare the performance of several different UAV trajectory tracking algorithms in normal and abnormal flight conditions to investigate the fault‐tolerant capabilities of a novel immunity‐based adaptive mechanism.

The evaluation of these algorithms is performed using the West Virginia University (WVU) UAV simulation environment. Three types of fixed‐parameter algorithms are considered as well as their adaptive versions obtained by adding an immunity‐based mechanism. The types of control laws investigated are: position proportional, integral, and derivative control, outer‐loop nonlinear dynamic inversion (NLDI), and extended NLDI. Actuator failures on the three channels and increased turbulence conditions are considered for several different flight paths. Specific and global performance metrics are defined based on trajectory tracking errors and control surface activity.

The performance of all of the adaptive controllers proves to be better than their fixed parameter counterparts during the presence of a failure in all cases considered.

The immunity inspired adaptation mechanism has promising potential to enhance the fault‐tolerant capabilities of autonomous flight control algorithms and the extension of its use at all levels within the control laws considered and in conjunction with other control architectures is worth investigating.

The WVU UAV simulation environment has been proved to be a valuable tool for autonomous flight algorithm development, testing, and evaluation in normal and abnormal flight conditions.

A novel adaptation mechanism is investigated for UAV control algorithms with fault‐tolerant capabilities. The issue of fault tolerance of UAV control laws has only been addressed in a limited manner in the literature, although it becomes critical in the context of imminent integration of UAVs within the commercial airspace.

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.

The purpose of this paper is to gain trajectory-tracking controllers for autonomous aircraft are optimized using a modified evolutionary, or genetic algorithm (GA).

The GA design utilizes real representation for the individual consisting of the collection of all controller gains subject to tuning. The initial population is generated randomly over pre-specified ranges. Alternatively, initial individuals are produced as random variations from a heuristically tuned set of gains to increase convergence time. A two-point crossover mechanism and a probabilistic mutation mechanism represent the genetic alterations performed on the population. The environment is represented by a performance index (PI) composed of a set of metrics based on tracking error and control activity in response to a commanded trajectory. Roulette-wheel selection with elitist strategy are implemented. A PI normalization scheme is also implemented to increase the speed of convergence. A flexible control laws design environment is developed, which can be used to easily optimize the gains for a variety of unmanned aerial vehicle (UAV) control laws architectures.

The performance of the aircraft trajectory-tracking controllers was shown to improve significantly through the GA optimization. Additionally, the novel normalization modification was shown to encourage more rapid convergence to an optimal solution.

The GA paradigm shows much promise in the optimization of highly non-linear aircraft trajectory-tracking controllers. The proposed optimization tool facilitates the investigation of novel control architectures regardless of complexity and dimensionality.

The addition of the evolutionary optimization to the WVU UAV simulation environment enhances significantly its capabilities for autonomous flight algorithm development, testing, and evaluation. The normalization methodology proposed in this paper has been shown to appreciably speed up the convergence of GAs.

The paper provides a flexible generalized framework for UAV control system evolutionary optimization. It includes specific novel structural elements and mechanisms for improved convergence as well as a comprehensive PI for trajectory tracking.

This paper aims to describe the design, development and flight-simulation testing of an artificial immune-system-based approach for accommodation of different aircraft sub-system failures/damages.

The approach is based on building an artificial memory, which represents self- (nominal conditions) and non-self (abnormal conditions) within the artificial immune system paradigm. Self- and non-self are structured as a set of memory cells consisting of measurement strings, over pre-defined time windows. Each string is a set of features values at each sample time of the flight. The accommodation algorithm is based on the cell in the memory that is the most similar to the in-coming measurement. Once the best match is found, control commands corresponding to this match are extracted from the memory and used for control purposes.

The results demonstrate the possibility of extracting pilot compensatory commands from the self/non-self structure and capability of the artificial-immune-system-based scheme to accommodate an actuator malfunction, maintain control and complete the task.

This paper concentrates on investigation of the possibility of extracting compensatory pilot commands. This is a preliminary step toward a more comprehensive solution to the aircraft abnormal condition accommodation problem.

The results demonstrate the effectiveness of the proposed approach using a motion-based flight simulator for actuator and sensor failures.

This research effort is focused on investigating the use of the artificial immune system paradigm for control purposes based on a novel methodology.

This paper aims to present the development of prediction models for aircraft actuator failure impact on flight envelope within the artificial immune system (AIS) paradigm.

Simplified algorithms are developed for estimating ranges of flight envelope-relevant variables using an AIS in conjunction with the hierarchical multi-self strategy. The AIS is a new computational paradigm mimicking mechanisms of its biological counterpart for health management of complex systems. The hierarchical multi-self strategy consists of building the AIS as a collection of low-dimensional projections replacing the hyperspace of the self to avoid numerical and conceptual issues related to the high dimensionality of the problem.

The proposed methodology demonstrates the capability of the AIS to not only detect and identify abnormal conditions (ACs) of the aircraft subsystem but also evaluate their impact and consequences.

The prediction of altered ranges of relevant variables at post-failure conditions requires failure-specific algorithms to correlate with the characteristics and dimensionality of self-projections. Future investigations are expected to expand the types of subsystems that are affected and the nature of the ACs targeted.

It is expected that the proposed methodology will facilitate the design of on-board augmentation systems to increase aircraft survivability and improve operation safety.

The AIS paradigm is extended to AC evaluation as part of an integrated and comprehensive health management process system, also including AC detection, identification and accommodation.

The purpose of this paper is to present the development of a Matlab/Simulink‐based simulation environment for the design and testing of indirect and direct adaptive flight control laws with fault tolerant capabilities to deal with the occurrence of actuator and sensor failures.

The simulation environment features a modular architecture and a detailed graphical user interface for simulation scenario set‐up. Indirect adaptive flight control laws are implemented based on an optimal control design and frequency domain‐based online parameter estimation. Direct adaptive flight control laws consist of non‐linear dynamic inversion performed at a reference nominal flight condition augmented with artificial neural networks (NNs) to compensate for inversion errors and abnormal flight conditions following the occurrence of actuator or sensor failures. Failure detection, identification, and accommodation schemes relying on neural estimators are developed and implemented.

The simulation environment provides a valuable platform for the evaluation and validation of fault‐tolerant flight control laws.

The modularity of the simulation package allows rapid reconfiguration of control laws, aircraft model, and detection schemes. This flexibility allows the investigation of various design issues such as: the selection of control laws architecture (including the type of the neural augmentation), the tuning of NN parameters, the selection of parameter identification techniques, the effects of anti‐control saturation techniques, the selection and the tuning of the control allocation scheme, as well as the selection and tuning of the failure detection and identification schemes.

The novelty of this research efforts resides in the development and the integration of a comprehensive simulation environment allowing a very detailed validation of a number of control laws for the purpose of verifying the performance of actuator and sensor failure detection, identification, and accommodation schemes.

The paper aims to present the development of a detection scheme for pilot fatigue using fuzzy logic. Evaluation parameters based on the dynamic response of the pilot/aircraft system are to be defined and criteria for online fatigue detection to be formulated.

The approach is based on the idea that, while performing the same task, under otherwise identical conditions, the dynamic signatures of the pilot/aircraft system are different depending on the pilot condition, “rested” or “tired.” Tests performed on a 6 degrees‐of‐freedom (DOF) flight simulator with pilots at two extreme levels of alertness are used to define parameters based on aircraft states and pilot input measurements that can serve as pilot fatigue detectors at steady state flight conditions. These parameters are computed using the statistics of the tracking errors (TE), state and control time histories, and the Fourier transforms of the TE. Fuzzy logic is used to evaluate the pilot condition based on composite detection parameters.

Validation tests on a 6 DOF flight simulator showed that the proposed detection scheme has promising capabilities for safety monitoring purposes and design of control laws that can accommodate for pilot abnormal conditions.

The pilot fatigue detection algorithm presented in this paper can be used as a starting point for future research in the following directions: development of safety monitoring systems for warning and/or triggering of automatic control compensation; development of pilot fault‐tolerant control laws; development of human pilot models for simulation, handling qualities assessment, and control laws design.

The approach for pilot fatigue detection proposed in this paper is a viable alternative to existing methods based on physiological measurements such as electrical activity of the brain, pulse, body temperature, etc. which imply direct and permanent connection of the pilot to the measurement system and interfere adversely with pilot comfort and his/her ability to perform the task. The proposed approach eliminates this drawback and does not require on‐board additional heavy equipment.

Pilot fatigue assessment from measurements of pilot/aircraft dynamic parameters has not yet been investigated as an alternative to the physiological approach.