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

Abstract

PurposeThis paper aims to design an adaptive nonlinear strategy capable of timely detection and reconstruction of faults in the attitude’s sensors of an autonomous aerial vehicle with greater accuracy concerning other conventional approaches in the literature.Design/methodology/approachThe proposed scheme integrates a baseline nonlinear controller with an improved radial basis function neural network (IRBFNN) to detect different kinds of anomalies and failures that may occur in the attitude’s sensors of an autonomous aerial vehicle. An integral sliding mode concept is used as auto-tune weight update law in the IRBFNN instead of conventional weight update laws to optimize its learning capability without computational complexities. The simulations results and stability analysis validate the promising contributions of the suggested methodology over the other conventional approaches.FindingsThe performance of the proposed control algorithm is compared with the conventional radial basis function neural network (RBFNN), multi-layer perceptron neural network (MLPNN) and high gain observer (HGO) for a quadrotor vehicle suffering from various kinds of faults, e.g. abrupt, incipient and intermittent. From the simulation results obtained, it is found that the proposed algorithm’s performance in faults detection and estimation is relatively better than the rest of the methodologies.Practical implicationsFor the improvement in the stability and safety of an autonomous aerial vehicle during flight operations, quick identification and reconstruction of attitude’s sensor faults and failures always play a crucial role. Efficient fault detection and estimation scheme are considered indispensable for an error-free and safe flight mission of an autonomous aerial vehicle.Originality/valueThe proposed scheme introduces RBFNN techniques to detect and estimate the quadrotor attitude’s sensor faults and failures efficiently. An integral sliding mode effect is used as the network’s backpropagation law to automatically modify its learning parameters accordingly, thereby speeding up the learning capabilities as compared to the conventional neural network backpropagation laws. Compared with the other investigated techniques, the proposed strategy achieve remarkable results in the detection and estimation of various faults.

Chaos Detection and Mitigation in Swarm of Drones Using Machine Learning Techniques and Chaotic Attractors

“…For example, an end to end fault analysis framework for a single micro aerial vehicle that only considers anomalies with obstacle detections [54]. Some studies used artificial neural network for sensor-based fault detection [55]. As a result, this current study proposes a machine learning-based, data-driven methodology for detecting chaotic anomalies in swarm flights.…”

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

“…Sliding-mode control (SMC) is considered one of the influencing control schemes for nonlinear systems affected by exogenous disturbances (Zhihong et al, 1994;Ullah et al, 2021aUllah et al, , 2021bZou, et al, 2011). In Jia et al (2017), Parma et al (2003), Mu et al (2017) and Hassani et al (2019), SMC and back-stepping techniques are proposed to get rid of the problems associated with the linear controllers.…”

“…Because of their significant performance, they have now been widely used in the quadrotor's control. They can significantly improve the motion control of the quadrotor by adopting variations in system dynamics and controller parameters according to the conditions (Alqaisi et al, 2019;Moawad et al, 2019;Ullah et al, 2021aUllah et al, , 2021bLuo et al, 2019). For example, for a quadrotor following desired position and attitude in the presence of disturbances and parametric uncertainties, an adaptive SMC based on a neural network (NN) is proposed in Razmi and Afshinfar (2019).…”