International Journal of Adaptive Control and Signal Processing最新文献

Intrustion Detection System (IDS) refers to the gear or software that monitors a network or system for malicious activity or policy violations. Periodically, the system records any intrusion action or breach, which frequently modifies the administrator. Cyber Physical System (CPS) is particularly called as networked connected system, in which the system components are spatially distributed and integrated via the communication network. The control mechanism ensures computation significance; however, the system does affect attacks. Researchers are trying to handle this issue via the existing anomaly datasets. In this way, this paper follows an intrusion detection system under three major stages including extraction of features, selection of feature, and detection. The primary stage is the extraction of Statistical features like standard deviation, mean, mode, variance, and median, as well as higher-order statistical features like moment, percentile, improved correlation, kurtosis, mutual information, skewness, flow-based features, and information gain-based features. The curse of dimensionality becomes a significant problem in this scenario, so it is crucial to choose the right features. Improved Linear Discriminant Analysis (LDA) is utilized to choose the right features. The selected features are subjected to a Hybrid classifier for final detection. Here, models like CNN (Convolutional Neural Network) and Bi-GRU (Bidirectional Gated Recurrent Unit) are combined. A new Bernoulli Map Estimated Arithmetic Optimization Algorithm (BMEAOA) is added to train the system by adjusting the ideal weights of the two classifiers, leading to improved detection outcomes. Ultimately, the effectiveness is assessed in comparison to the other traditional techniques.

This article looks into the dynamic event-triggered fixed-time adaptive attitude control problem for nonlinear six-rotor unmanned aerial vehicle (UAV) with external disturbances. The multiple six-rotor UAVs considered are regarded as nonlinear multi-agent systems (MASs), and each subsystem has multiple inputs. Under the framework of backstepping recursive design, an effective adaptive fixed-time control method is proposed by combining neural networks (NNs) technology and fixed-time theory. NNs are utilized to handle unknown nonlinearity and unmodeled parts in attitude systems. The hyperbolic tangent function is ushered to address the singularity problem that may occur in the derivative of the controller, thereby averting the phenomenon of chattering. For the sake of alleviating the correspondence burden of multiple UAVs attitude systems, a modified dynamic event-triggered mechanism (DETM) is ushered. The developed controller swears for that all signals of the six-rotor UAV attitude systems are bounded and the tracking errors converge to a small neighborhood of the origin within a fixed-time interval. Eventually, with the help of simulation results, the effectiveness of the proposed control scheme was verified.

This article addresses the position control issue of multi-quadrotor unmanned aerial vehicle (QUAV) formation. Concerning the translational dynamic of a multi-QUAV system, on the one hand, it is an under-actuation dynamic; on the other hand, it does not satisfy the matching condition. These features will cause inevitable thorny in the formation position control design. Furthermore, because of the state coupling problem, the formation control of multi-QUAV system is more challenging and knotty than the control of single QUAV system. To achieve this control, both backstepping technique and neural network (NN) approximation strategy are combined by introducing an intermediary control, where NN is employed to compensate the system uncertainty. However, since the traditional adaptive NN control methods need to train a large number of adaptive parameters for the high approximation accuracy, it will cause the heavy computing burden if traditional adaptive method is used for the QUAV formation control. The proposed adaptive NN strategy in this paper only requires training a scalar adaptive parameter, which is generated from the norm of NN weight vector or matrix, thereby significantly reducing computational burden. Finally, according to Lyapunov stability proof and computer simulation, it is demonstrated that the control tasks can be successfully accomplished.

In this article, the asymptotic synchronization of a class of fuzzy inertial neural networks (FINNs) with time-varying delays is investigated. First, the direct analysis approach is applied to replace the accustomed variable transformation and the reduced-order method for addressing the inertial term. Second, a suitable Lyapunov function and control scheme are devised to obtain several new sufficient conditions to guarantee the asymptotic synchronization of the class of FINNs with time-varying delays. It turns out that the obtained criteria are simpler and more effective. Meanwhile, some numerical examples demonstrate the effectiveness of the proposed strategies and verify the theoretical results.

The problem of adaptive fuzzy fixed-time tracking control based on a category of nonlinear systems with unmodeled dynamics and dynamic disturbances is investigated in this article. By introducing the novel fixed-time dynamic signal, the unmodeled dynamics can be disposed of. On the basis of the fuzzy logic systems (FLSs) and adaptive backstepping technology, the disposing difficulty of the unknown nonlinear portions is reduced. Then, all signals in the closed-loop system are ensured to be bounded under the fixed-time Lyapunov stability theory. Simultaneously, the tracking error converges to a small neighborhood of the origin. Eventually, simulation consequences reveal the validity of the presented control method.

This article studies the adaptive neural networks (NNs) asymptotic tracking control of high-order nonlinear systems subject to prescribed performance, non-strict-feedback structure, and output constraints. To address the output constraint issue while guaranteeing that the tracking error stays within the specified area, a variable fused with the time-varying constraint functions is introduced. Then, a pivotal form of coordinate transformation is developed, which plays a key role in achieving asymptotic tracking performance. Based on the backstepping and Lyapunov method, the designed control scheme assures that all system variables are semi-globally uniformly ultimately bounded, the output constraints are never broken, and the tracking error always stays within the predefined function and asymptotically converges to zero. Finally, the effectiveness of theoretical findings is verified via simulation studies.

This paper proposes a new detection, reconstruction and mitigation scheme for nonlinear cyber-physical systems experiencing deception attacks in controller-actuator channel. For early detection of attacks, an anomaly detection unit based on the diagnostic observer is designed. After residual generation and evaluation, attack is detected. Upon attack detection, an adaptive fuzzy wavelet network (FWN) as an online nonlinear estimator is activated to reconstruct the detected malicious attack. Then, attack mitigation mechanism based on the command filtered backstepping approach and reconstructed attack is activated to mitigate the adverse effect of the detected attack. Stability analysis of the suggested strategy is presented and simulation results are provided to show the effectiveness of the suggested scheme.

We consider the problem of stochastic identification of multiple sinusoids from intermittently missing measurements of superimposed signal. An alternate problem formulation is presented as estimation of amplitude and frequency of the sinusoids from missing measurements. The popularly known estimation methods, such as the extended Kalman filter (EKF) and cubature Kalman filter (CKF) may fail or suffer from poor accuracy if the measurements are missing. In this paper, we redesign the EKF to handle this irregularity in measurements and apply the modified EKF for the formulated estimation problem. In this regard, we introduce a modified measurement model incorporating the possibility of missing measurements. Subsequently, we rederive the relevant parameters of the EKF, such as measurement estimate, measurement error covariance, and state-measurement cross-covariance, for the modified measurement model. Furthermore, we rederive the posterior covariance with minimized trace and study the stability of the resulting extension of the EKF. The results reveal the superior performance of the modified EKF compared with the ordinary Gaussian filters and existing filters-based estimation of the sinusoids in the presence of intermittently missing measurements.