Adaptive rapid neural observer-based sensors fault diagnosis and reconstruction of quadrotor unmanned aerial vehicle

Abstract

Purpose The objective of this research is to investigate various neural network (NN) observer techniques for sensors fault identification and diagnosis of nonlinear system in consideration of numerous faults, failures, uncertainties and disturbances. For the importunity of increasing the faults diagnosis and reconstruction preciseness, a new technique is used for modifying the weight parameters of NNs without enhancement of computational complexities. Design/methodology/approach Various techniques such as adaptive radial basis functions (ARBF), conventional radial basis functions, adaptive multi-layer perceptron, conventional multi-layer perceptron and extended state observer are presented. For increasing the fault detection preciseness, a new technique is used for updating the weight parameters of radial basis functions and multi-layer perceptron (MLP) without enhancement of computational complexities. Lyapunov stability theory and sliding-mode surface concepts are used for the weight-updating parameters. Based on the combination of these two concepts, the weight parameters of NNs are updated adaptively. The key purpose of utilization of adaptive weight is to enhance the detection of faults with high accuracy. Because of the online adaptation, the ARBF can detect various kinds of faults and failures such as simultaneous, incipient, intermittent and abrupt faults effectively. Results depict that the suggested algorithm (ARBF) demonstrates more confrontation to unknown disturbances, faults and system dynamics compared with other investigated techniques and techniques used in the literature. The proposed algorithms are investigated by the utilization of quadrotor unmanned aerial vehicle dynamics, which authenticate the efficiency of the suggested algorithm. Findings The proposed Lyapunov function theory and sliding-mode surface-based strategy are studied, which shows more efficiency to unknown faults, failures, uncertainties and disturbances compared with conventional approaches as well as techniques used in the literature. Practical implications For improvement of the system safety and for avoiding failure and damage, the rapid fault detection and isolation has a great significance; the proposed approaches in this research work guarantee the detection and reconstruction of unknown faults, which has a great significance for practical life. Originality/value In this research, two strategies such Lyapunov function theory and sliding-mode surface concept are used in combination for tuning the weight parameters of NNs adaptively. The main purpose of these strategies is the fault diagnosis and reconstruction with high accuracy in terms of shape as well as the magnitude of unknown faults. Results depict that the proposed strategy is more effective compared with techniques used in the literature.

Identification of combined sensor faults in structural health monitoring systems

“…Biddle and Fallah (2021) have introduced an identification approach for faults occurring simultaneously at multiple sensors in autonomous vehicle controllers via multiclass SVM models. Taimoor et al (2021) have proposed using adaptive radial basis functions for sensor fault identification or quadrotor unmanned aerial vehicles. Aiming to identify sensor faults of gas turbine engines, Zhu et al (2020) have introduced a residual-based scheme to identify single and simultaneous sensor faults occurring at different sensors.…”

Fault-tolerant tracking control for a non-linear twin-rotor system under ellipsoidal bounding

“…In the case of unmanned aerial vehicles (UAVs), the problem of ensuring reliability and control (Abbaspour * Corresponding author et al, 2018;Hamadi et al, 2020;Altan and Hacıoglu, 2020;Prochazka and Stomberg, 2020;Taimoor et al, 2021) is particularly important because they are characterized by high dynamics and functioning in varying conditions with a large amount of disturbances. In order to ensure the reliable operation of a UAV, various methods of fault detection and estimation of the remaining useful life of the vehicle components can be used (Zhang et al, 2021;Sadhu et al, 2020;Camci et al, 2019;Rodrigues et al, 2018;Petritoli et al, 2018).…”

“…An overview of FTC methods can be found the works of Veremey (2021), Habibi et al (2019, Li et al (2019), Hamdi et al (2021 and Witczak (2014). Moreover, interesting theoretical studies and practical applications of active FTC methods in the case of faulty sensors (Manohar and Das, 2020;Wang, 2020;Azzoug et al, 2021;Taimoor et al, 2021) and actuators (Hamadi et al, 2020;Prochazka and Stomberg, 2020;Yu et al, 2021;Liu et al, 2021) can also be found.…”

Improved neural network-based sensor fault detection and estimation strategy for an autonomous aerial vehicle

“…Any failure in these sensors can lead to severe losses and human injuries due to the presence of faults and disturbances (Taimoor and Aijun, 2020). Since the sensor faults cannot be avoided in practical conditions which may result in the system’s performance degradation, quick diagnosis of such faults are greatly appreciated in making the flight operations of UAV reliable and safe (Taimoor et al , 2021). Once the faults are identified, necessary actions can be taken to cancel out their effect to guarantee a successful flight mission.…”