International Journal of Adaptive Control and Signal Processing最新文献

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.

In this article, the problem of intermittent fault (IF) detection is investigated for nonstationary processes in the multivariate statistics framework. By combining the moving window technique with maximum likelihood estimation (MLE), the moving window Wald-based control chart is proposed to realize the detection of IFs in nonstationary processes. The computational efficiency and the convergence properties are discussed for the designed iterative algorithm of MLE. Then, necessary and sufficient conditions are presented to guarantee the detectability of IFs with the consideration of window lengths. Moreover, the alarm delays are analyzed for the appearance and disappearance of IFs. In virtue of the above analysis, the optimal window length is derived by minimizing the supremum of alarm delays. In order to estimate the time of IFs' appearance and disappearance, an algorithm is designed with the inspiration of simulated annealing strategy. Finally, a simulation on rotary steerable drilling tool system is provided to verify the effectiveness of the proposed method.

An adaptive event-triggered scheme is considered for networked control systems subject to deception and reply cyber attacks. In particular, the adaptive event-triggered mechanism is used in the closed-loop controller design to reduce signal transmission and communication burden. The attacker destroys the system's performance by employing deception attacks on sensor-to-controller communication channels and replay attacks on controller-to-actuator communication channels, respectively. By utilizing the Lyapunov stability approach, the closed-loop system guarantees mean square stability and ensure security. The adaptive event-triggered controller gains are presented by solving a set of matrix inequalities. Finally, a simulation result including the model of a batch reactor is presented to demonstrate the efficiency of the methods proposed.

This article focuses on the outlier-resistant state estimation problem for discrete time-varying complex networks (TVCNs) affected by random false data injection attacks (FDIAs) under an encoding–decoding mechanism (EDM). From the perspective of information security, a uniform-quantization-based EDM is employed to encrypt the transmitted data. During the data transmission process, a set of independent random variables governed by Bernoulli distribution is introduced to characterize the occurrence of random FDIAs. For the purpose of alleviating the passive impact of potential measurement outliers, a saturation structure is adopted during the estimator design. The gain matrix is given by minimizing the upper bound of estimation error covariance. According to the stochastic analysis method, it is shown that the state estimation error is bounded exponentially in mean-square sense by providing new sufficient condition. It should be noted that we make the first attempt to develop new outlier-resistant state estimation method with performance evolution criterion in the time-varying perspective for TVCNs with random FDIAs under EDM. Finally, a simulation example with comparative experiment is presented to illustrate the effectiveness of the newly presented outlier-resistant estimation algorithm.

The productivity in the agricultural sector is minimized due to the disease in plants. In general, the ailments that affect plants are identified by the farmers and the losses are minimized, when the diseases are identified early. The early identification of leaf diseases is difficult in the traditional approaches. Hence, in this article, for detecting maize leaf disease, an adaptive competitive shuffled shepherd optimization-driven deep quantum neural network (adaptive CSSO-based deep QNN) is implemented. Here, the initial process is the simulation of the IoT nodes and the leaf data are collected. This data are transferred to base station (BS) via the best routes. The optimal routes are identified using the adaptive CCSO algorithm. The adaptive concept, shuffled shepherd optimization algorithm (SSOA) and competitive swarm optimizer (CSO) are merged for forming the adaptive-CSSO algorithm. The leaf detection is done in the BS and initially, the data is preprocessed using region of interest (ROI). Then, the relevant features are extracted. Finally, the disease in the maize leaf is detected using Deep QNN and the training is done by adaptive CSSO. The devised approach has maximum accuracy of 96.04%, sensitivity of 97.41%, specificity of 94.35%, energy of 0.01 J, and minimum delay of 0.9596 s.