Reconstruction-based anomaly detection is appealing for industrial inspection because it reconstructs anomaly-free references and produces interpretable pixel-level residual maps. However, in multiclass settings, it often suffers from, first, identity mapping, where abnormal regions are overreconstructed and residuals vanish, and second, cross-category feature entanglement, which introduces artifacts and weakens localization. Residual scoring is further biased because it mixes true defect discrepancies with model-induced reconstruction errors, causing false alarms and confusing explanations. We propose a semantically guided counterfactual model (SGCAD) for multiclass anomaly detection and localization. SGCAD applies a masking intervention to a candidate region, reconstructs the intervened input, and contrasts factual and intervened residuals to estimate reconstruction bias, yielding a debiased anomaly map by treating consistent differences as anomalies. To stabilize reconstruction, we introduce a dual-branch bottleneck with latent constraints to mitigate identity mapping while preserving normal details, and a hierarchical adaptive prompt to reduce cross-category confusion during decoding without class priors. Extensive experiments on four public benchmarks demonstrate that while SGCAD maintains competitive image-level detection performance, it achieves superior state-of-the-art results in pixel-level localization. By effectively mitigating bias-induced false positives, SGCAD offers a more precise and reliable solution for practical industrial inspection.

This paper focuses on the adaptive neural network output feedback control problem for heterogeneous vehicle platoon system (HVPS) under the condition of sensor deception attack. Firstly, an event-triggered mechanism by double-channel transmissions is constructed, which considers both controller triggering and output triggering, effectively reducing the waste of communication resources and unnecessary data transmission. Secondly, in order to estimate the unmeasured states in the system, a neural network state observer is designed. It takes the output signals caused by output triggering and sensor failures as input to generate usable state estimates and ensure the smooth operation of the backstepping method. In addition, barrier Lyapunov functions (BLFs) are used to impose constraints on vehicle spacing errors to ensure that the errors will not expand infinitely. Combined with dynamic surface control (DSC) technology, an adaptive event-triggered output feedback control scheme based on backstepping is proposed in this paper. It guarantees both individual vehicle stability and weak string stability of HVPS. Through Lyapunov stability analysis, it is proved that all signals remain bounded. Finally, a simulation example verifies the effectiveness of the proposed method.

In this article, in the occurrence of load disturbances and internal actuator faults, exact performance compensation problem for nonlinear interconnected power systems (NIPSs) integrated with wind farms is solved by presenting an observer-based sliding mode control strategy. Concretely, a dynamic event condition in the sensor channel is designed by virtue of triggered output error and a dynamic threshold. With the help of event outputs and neural networks (NNs) technology, a composite observer is proposed to perform state, disturbance, and fault estimations of the power system simultaneously. By utilizing the variable estimations, a novel sliding mode manifold is designed and an observer-based controller is derived directly. Intuitively, the compensation action is triggered once the unknown variables of the NIPSs are estimated, thereby the disturbance rejection and fault tolerance can be achieved. A prominent benefit of the presented strategy is that the estimation convergence ability of the observer is proved independently, then the relationship between the observer and controller for the NIPSs can be relaxed. The control ability is analyzed within aH∞performance through the Lyapunov theory. Simulation results for the two area NIPSs and comparisons on the recent achievements of the NIPSs are made, the results show that more than ten percent improvements can be realized compared with the recent control methods, which in return demonstrate the compensation performance of the presented control strategy.

This paper concerns the observer-based dual-memory control for Lurie-type cyber-physical systems (CPSs) subject to denial of service (DoS) attacks. First, an augmented switching Lurie-type CPSs model is established to analyze the impact of DoS attacks and synergy time delays on controller performance. Second, the memory-dynamic event-triggered scheme (MDETS) efficiently utilizes bandwidth resources by adapting the number of historical data packets transmitted based on system state fluctuations, meanwhile, the controller design incorporates constant time-delay compensation to improve system stability. Third, the exponential stability conditions for the closed-loop Lurie-type CPSs are derived via Lyapunov theory. Additionally, the co-design of the observer gain, controller gain, and event-triggering matrices is formulated using linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed control scheme is validated through application to a DC motor system.

In this article, we focus on the issue of adaptive fuzzy finite-time tracking control for a class of uncertain fractional-order nonlinear systems with external disturbance. A new finite-time fractional-order command filtered implementation scheme is presented for the adaptive backstepping method. In the command filtered implementation approach, analytical computation of the fractional derivatives of the stabilizing functions is not necessary. Therefore, the controller and adaptive law of the systems are easier to derive and implement. Based on the proposed command filter, adaptive backstepping technique, and fractional Lyapunov’s direct method, a novel adaptive fuzzy finite-time controller is designed, which can guarantee that the tracking error converges to a small neighborhood of the origin in finite time, and other signals of the closed-loop systems are semiglobal uniform ultimate bounded. In the design process of the controller, fuzzy logic systems (FLSs) are employed to approximate the unknown nonlinear functions, and an auxiliary function is adopted to compensate for the unknown external disturbance and approximation errors of FLSs. Finally, a simulation example is given to show the effectiveness and availability of the proposed control strategy.

In this paper, the tracking control problem for four-wheel independently actuated (FWIA) electric vehicles (EVs) system with actuator fault and unmatched disturbance is investigated. An adaptive fast terminal sliding mode fault-tolerant control (AFTSMFTC) scheme is proposed. First of all, the FWIA electric vehicle (EV) system with actuator fault and unmatched disturbance model is established. Then, a novel composite observer is designed to estimate the state and disturbance of the EV system simultaneously; by introducing an updating law, the fault efficiency factor is reconstructed. Furthermore, in accordance with the estimated information, an AFTSMFTC strategy is proposed based on the tracking error and its derivative information. In the AFTSMFTC scheme, on one hand, two adaptive expressions are contained to realize a self-adapting adjustment to the controller parameters, such that a faster convergence can be guaranteed; on the other hand, the estimated disturbance value is utilized to design the scheme in order to improve the robustness of the tracking performance. Finally, three cases are given to illustrate the effectiveness of the proposed method.

In this article, a novel two-channel event-triggered adaptive fault-tolerant control approach is designed to handle the degree freedom nonlinear system with disturbances. The main purpose of this article is to maintain the output tracking ability while reducing the transmission burden. The objective is realized by designing a novel adaptive sliding mode controller integrated with the event trigger mechanism. Concretely, the state, actuator fault, and lumped disturbance information are obtained by designing a composite observer, in which the disturbance upper bound is not needed. Then, the obtained information is utilized to design the controller. In the proposed method, the lumped disturbance and nonlinear estimated values are contained in the sliding mode manifold to enhance the robustness. Additionally, an adaptive parameter is also integrated into the controller to compensate for the disturbance. Furthermore, a specific event-triggered controller is proposed to keep the tracking ability and to reduce the transmission load of the fault system. The no Zeno phenomenon performance is analyzed and, finally, the applications to a suspension system and a two-tank system are given to show the fault tolerance ability of the proposed method.