Hao Liu

This paper proposes a region-topology-based estimation method for state estimation and fault diagnosis in hydroturbine control systems. A set observer is designed to handle bounded noise and disturbances, and the gain is optimized using linear matrix inequalities to improve accuracy. A threshold method based on the residual center paradigm is employed to determine faults by examining whether the residual region topology contains the origin. A discrete system model is established, and the dynamic boundary of the estimation error is derived. Numerical simulations validate the effectiveness of the method.

This paper investigates set-valued state estimation of cyber-physical systems (CPSs) with unknown-but-bounded (UBB) noises. Note that constrained polynomial zonotopes (CPZs) are utilized to characterize both convex and non-convex sets of noises. However, privacy issues should be taken into account since out-sourcing the set-valued operations to the cloud-based node is required when collecting measurements from distributed sensors. In order to address this issue, two different set-valued estimation protocols employing partially homomorphic encryption (PHE) are proposed to guarantee the corresponding privacy. Further-more, it is proved that the proposed protocols can ensure privacy against sensor coalition, cloud coalition, and user coalition, respectively. Finally, numerical examples are provided to show the advantages and effectiveness of our results.

This article concentrates on the attack detection and active attack defense strategies for discrete-time linear cyber–physical systems (CPSs) with unknown but bounded (UBB) disturbance and noise in the presence of both actuator and sensor attacks. First, a novel zonotopic observer is constructed to estimate the set-valued state and actuator attack by introducing augmentation techniques. To mitigate the effects of uncertainty and enhance estimation accuracy, the $H_{\infty }$ technique is introduced to construct the observer. Unlike most existing works, the constructed observer simultaneously estimates the system state and actuator attacks. Then, by combining the designed observer with reachability analysis, a set-valued abnormal detector and a residual-based abnormal detector are designed to detect actuator and sensor attacks, respectively. In addition, by incorporating the obtained state reachable sets and the $H_{\infty }$ technique, an active attack defense mechanism is designed to mitigate the impact of attacks on system performance. The proposed defense strategy does not introduce any performance loss in the absence of attacks. Finally, the superiority of the developed method is demonstrated by its application to a numerical simulation and an autonomous aircraft system.

In this paper, encrypted set-based estimation (ESE) is investigated for cyber-physical systems (CPSs) with unknown-but-bounded (UBB) noises, which permits to outsource the state estimation of privacy-sensitive data via public networks to the third-party platforms. To prevent the leakage of the key information by eavesdropping, homomorphic encryption approach is employed. First, the classic Paillier encryption is utilized to encrypt data and the effect caused by the quantization error is analyzed as well. Then, (2,2)-threshold secret sharing encryption scheme is proposed to reduce the computation burden by using two shares by assuming that the two clouds are not colluding, and the corresponding security is further discussed. Finally, numerical simulations are given to illustrate the validity of the main results.

In this paper, resilient distributed set-based estimation is investigated for cyber-physical systems (CPSs) with unknown-but-bounded (UBB) noises which are characterized by constrained polynomial zonotopes (CPZs). Both generalized intersection of CPZs and diffusion strategy are developed to calculate the measurement update and obtain the estimation set. When the system is vulnerable to malicious false-data-injection (FDI) attacks, the encoding-decoding approach is proposed to preserve privacy, which can also be utilized to improve the attack detection rate. After detecting attacks, the corresponding resilient estimation algorithm is provided to alleviate the impact introduced by attacks. Finally, numerical simulations are provided to illustrate the validity of the presented approaches.

This work investigates the problems of attack detection and reconstruction for discrete-time linear cyber-physical systems (CPSs) with unknown-but-bounded disturbances and measurement noise. First, an improved unknown input observer (UIO) with fewer synthesis conditions is proposed to estimate the system state. To enhance the estimation accuracy, a decoupling method is proposed to decouple the disturbance matrix and exclude the effects of disturbances on the error system. Then, to reduce the computational complexity, an improved attack detection method based on the reachability analysis of the residual is proposed, which requires less computation than the existing attack detection methods. Moreover, the impact of disturbances and measurement noise on the process of attack reconstruction and isolation is analyzed. The accuracy of attack reconstruction is improved through the decoupling approach and $H_{\infty } $ technology. Finally, the simulation results verify the effectiveness and feasibility of the proposed approach.