Neural-sliding mode approach-based adaptive estimation, isolation and tolerance of aircraft sensor fault

Abstract

Purpose The purpose of this paper is to propose an adaptive neural-sliding mode-based observer for the estimation and reconstruction of unknown faults and disturbances for time-varying nonlinear systems such as aircraft, to ensure preciseness in the diagnosis of fault magnitude as well as the shape without enhancement of system complexity and cost. Fault-tolerant control (FTC) strategy based on adaptive neural-sliding mode is also proposed in the existence of faults for ensuring the stability of the faulty system. Design/methodology/approach In this paper, three strategies are presented: adaptive radial basis functions neural network (ARBFNN), conventional radial basis functions neural network (CRBFNN) and integral-chain differentiator. For the purpose of enhancement of fault diagnosis and isolation, a new sliding mode-based concept is introduced for the weight updating parameters of radial basis functions neural network (RBFNN).The main objective of updating the weight parameters adaptively is to enhance the effectiveness of fault diagnosis and isolation without increasing the computational complexities of the system. Results depict the effectiveness of the proposed ARBFNN approach in fault detection (FD) and approximation compared to CRBFNN, integral-chain differentiator and schemes existing in literature. In the second step, the FTC strategy is presented separately for each observer in the presence of unknown faults and failures for ensuring the stability of the system, which is validated on Boeing 747 100/200 aircraft. Findings The proposed adaptive neural-sliding mode approach is investigated, which depicts more effectiveness in numerous situations such as faults, disturbances and uncertainties compared to algorithms used in literature. In this paper, both the fault approximation and isolation and the fault tolerance approaches are studied. Practical implications For the enhancement of safety level as well as for avoiding any kind of damage, timely FD and fault tolerance have always had a significant role; therefore, the algorithms proposed in this research ensure the tolerance of faults and failures, which plays a vital role in practical life for avoiding any kind of damage. Originality/value In this study, a new neural-sliding mode concept is adopted for the adaptive faults approximation and reconstruction, and then the FTC algorithms are studied for each observer separately, whereas in previous studies, only the fault detection and isolation (FDI) or the fault tolerance problems were studied. Results demonstrate the effectiveness of the proposed strategy compared to the approaches given in the literature.

Improved radial basis function artificial neural network and exact-time extended state observer based non-singular rapid terminal sliding-mode control of quadrotor system

“…The main points of this work are summarized as follows: This paper introduces an IRBFANN approach, which is based on the exponential sliding mode effect to estimate and compensate for the uncertain nonlinear terms of the model such as time-variant mass and inertia matrix. The exponential sliding mode-based mechanism is adopted as the online weight update law of RBFANN, thereby increasing the accuracy of approximation without increasing computational complexities compared with Taimoor and Aijun (2020), where the conventional linear sliding-mode concept is used for the same purpose. A novel ETESO is proposed for the exact estimation of external and unknown high-order perturbations in exact time as compared to the traditional NDO such as in Maqsood and Qu, (2020), Maqsood et al (2020) and Shi et al (2018a, 2018b), where mostly the slow-time-varying and low-order external disturbances are studied. Hence, greater accuracy, lesser convergence time and higher efficiency are obtained compared with the other aforementioned approaches. The proposed ETESO is incorporated with NRTSMC with second power reaching law for the exact-time attitude and altitude tracking of a quadrotor system.…”

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

“…For which H ∞ technique is used for the purpose of stability as well as the consistency, theory of bi-index is used for the designing of FTC system. Many other algorithms and methodologies such as Kalman Filter SMO (Zhang et al , 2016; Djeghali et al , 2016), NNs (Chen et al , 2016; Taimoor and Aijun, 2019; Allen et al , 2016; Baghernezhad & horasani, 2016; Giorgi De et al , 2019; Fentaye et al , 2018; Yildirim and Kurt, 2019; Jia, and Duan, 2017; Amin et al , 2019; Amin et al , 2016; Taimoor and Aijun, 2020; Taimoor et al , 2020) and fuzzy logic (Ballesteros-Moncada et al , 2015) are implemented for the estimation of nonlinear parameters. In the above-mentioned techniques, NN techniques are better for faults identification because of the properties such as nonlinear function estimation property and learning abilities.…”

An Ensemble and Filtering-Based System for Predicting Educational Data Mining

“…Many data mining techniques have been exploited in this direction to achieve better insight of different academic data warehouses [2,3]. Extraction of significant knowledge from the warehouse plays a major role in propelling the wheel of further education by using various data mining techniques [4,5].When it comes to educational data mining, it is generally agreed that predicting student achievement is a critical responsibility. Researchers Zhu et al [5], conducted a case study to predict drop-out ratios using various classification approaches [6].…”