Immunity-Based Framework for Autonomous Flight  in GNSS-Denied Environment

Alnuaimi, Mohanad; Perhinschi, Mario G.; Al-Sinbol, Ghassan

doi:10.15866/irease.v12i6.16215

Cited by 4 publications

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“…The methodology does not require a sophisticated mathematical apparatus, it is data-driven, and is expandable and adaptive when new vetted data are available from regular system operation. The methodology has been implemented with promising results for UAVs as well (Sanchez et al, 2009;Alnuaimi et al, 2019). So far, the generation of the artificial immune system (AIS), a key element of the immunity paradigm, has been performed using clustering to structure the feature space and discriminate between self, or the normal system operation, and the nonself, or the abnormal operation.…”

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confidence: 99%

Partitioned artificial immune system for detection and identification of autonomous flight vehicle abnormal conditions

McLaughlin

Perhinschi

2020

IJIUS

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PurposeAn 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.Design/methodology/approachAn 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.FindingsThe 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.Research limitations/implicationsThe 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.Practical implicationsThe 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.Originality/valueIn 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.

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confidence: 99%

Partitioned artificial immune system for detection and identification of autonomous flight vehicle abnormal conditions

McLaughlin

Perhinschi

2020

IJIUS

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“…Two cases were considered to build the AIS; correction of the outputs of the estimation scheme represented by the velocity and position [83], and correction of the input of the estimation scheme represented by the acceleration [84].…”

Section: The Correction Featuresmentioning

confidence: 99%

Immunity-Based Framework for Autonomous Flight in GPS-Challenged Environment

Nuaimi¹

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“…Immunity based approaches have also been used for evaluation of aircraft actuator faults [52] over the extended flight envelope [53]. It has also been used for AC accommodation in situations of particular actuator locks [54] and in autonomous flight in a GNSS-denied environment [55]. The engine of the aircraft is another important system where the AIS could be applied.…”

Section: Chapter 2: Literature Reviewmentioning

confidence: 99%

Artificial Immune System for Unmanned Aerial Vehicle Abnormal Condition Detection and Identification

McLaughlin¹

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A detection and identification scheme for abnormal conditions was developed for an unmanned aerial vehicle (UAV) based on the artificial immune system (AIS) paradigm. This technique involves establishing a body of data to represent normal conditions referred to as "self" and differentiating these conditions from abnormal conditions, referred to as "non-self". Data collected from simulation of the UAV attempting to autonomously fly a pre-decided trajectory were used to develop and test a scheme that was able to detect and identify aircraft sensor and actuator faults. These faults included aerodynamic control surface locks and damages and angular rate sensor biases. The method used to create the AIS is known as the partition of the universe approach. This approach differs from standard clustering approaches because the universe is divided into uniform partition clusters rather than clustering data using some clustering algorithm. It is simpler and requires less computational resources. This will be the first time that this approach has been applied for use in aerospace engineering. Data collected from nominal flights were used to define self partitions, and the non-self partitions were defined implicitly. The creation scheme is also discussed, involving all software used for simulation, as well as the process of creating the self and the logic behind the detection and identification schemes. The detection scheme was evaluated based on detection rate, detection time, and false alarms for flights under both normal and abnormal conditions. The failure identification scheme was assessed in terms of identification rate and time. Investigation of the proposed technique showed promising results for the cases explored with comparable performance with respect to clustering-based approaches and motivates further research and extension of the proposed methodology toward a more complete health management system.

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Immunity-Based Framework for Autonomous Flight in GNSS-Denied Environment

Cited by 4 publications

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Partitioned artificial immune system for detection and identification of autonomous flight vehicle abnormal conditions

Partitioned artificial immune system for detection and identification of autonomous flight vehicle abnormal conditions

Immunity-Based Framework for Autonomous Flight in GPS-Challenged Environment

Artificial Immune System for Unmanned Aerial Vehicle Abnormal Condition Detection and Identification

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