In this paper, the robust adaptive control scheme based on backstepping technique is presented that improves the trajectory tracking performance of the quadrotor unmanned aerial vehicles (UAVs), specially tasked for supply, rescue and combat missions. The proposed control scheme is designed to estimate all the system parameters that may posses uncertainties and effectively rejects the completely unknown time varying external disturbances. The adaptive laws, derived through Lyapunov stability theorem are robustified by merging with derivative-integral (DI) term, resulting in rapid and accurate adaptation. In addition, to avoid parametric drift phenomenon, we introduce the projection modification (PM) in the designed DI-adaptive laws that ensures the closed-loop system signals bounded. The trajectory tracking and parameter estimation performance of the UAV in the presence of external disturbances, the payload pick up/drop off effect on altitude and recoil effect on attitude is analyzed by means of numerical simulations. The results validate strict robustness with extended applicability of proposed control scheme.

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In this article, an autonomous carrier landing problem of an aircraft is addressed by developing an autonomous carrier landing system (ACLS) composed of previewable guidance and control systems. In the guidance system, an appropriate touchdown point is estimated by predicting the seakeeping motion of the deck by unscented Kalman filtering technique, which is then utilized to adjust the reference glide path and produce an effective deck motion compensation, indispensable for a safe landing. The adaptive preview control (APC) scheme is proposed, which utilizes future reference information. The feedback and feedforward adaptive gains are derived through the Lyapunov stability theorem ensuring better tracking response and disturbance rejection. Hence, the asymptotic stability of the closed‐loop system is guaranteed. The simulation results depict better performance of the proposed ACLS in the presence of deck fluctuations and airwake disturbance compared with PID and LMI based preview control schemes.

Purpose The purpose of this paper is to design an innovative autonomous carrier landing system (ACLS) using novel robust adaptive preview control (RAPC) method, which can assure safe and successful autonomous carrier landing under the influence of airwake disturbance and irregular deck motion. To design a deck motion predictor based on an unscented Kalman filter (UKF), which predicts the touchdown point, very precisely. Design/methodology/approach An ACLS is comprising a UKF based deck motion predictor, a previewable glide path module and a control system. The previewable information is augmented with the system and then latitude and longitudinal controllers are designed based on the preview control scheme, in which the robust adaptive feedback and feedforward gain’s laws are obtained through Lyapunov stability theorem and linear matrix inequality approach, guarantying the closed-loop system’s asymptotic stability. Findings The autonomous carrier landing problem is solved by proposing robust ACLS, which is validated through numerical simulation in presence of sea disturbance and time-varying external disturbances. Practical implications The ACLS is designed considering the practical aspects of the application, presenting superior performance with extended robustness. Originality/value The novel RAPC, relative motion-based guidance system and deck motion compensation mechanism are developed and presented, never been implemented for autonomous carrier landing operations.

Before using any dataset in the intrusion detection system (IDS), it is crucial to acquire an accurate assessment of its efficiency. Nevertheless, the key complications presently met by the researchers are the deficiency of accessibility of any genuine assessment dataset and efficient metric for evaluating the enumerated quality of realism (QoR) of any internet of things (IoT)-based IDS dataset. It is challenging to obtain and gather data from real-world company setups owing to commercial continuousness and concerns such as integrity. This Letter presents a Sugeno fuzzy inference machine (SFIM)based metric method for assessing the QoR of existing IoT IDS datasets. Secondly, based on the results of the proposed metric, a synthetically precise next level IoT-based IDS dataset is aimed and produced, and an initial assessment showed to support the development of forthcoming IoT-IDS. This created dataset comprises both regular and irregular replications of present IoT-based network events happening at the precarious cyber structure in different companies. Finally, the QoR of the generated dataset is evaluated by means of the proposed metric and is compared with state-of-the-art commonly available compelling datasets for validating its supremacy.