Adaptive neural network control for nonlinear cyber-physical systems subject to false data injection attacks with prescribed performance

Liu, Zhijie; Tang, Jian; Zhao, Zhijia; Zhang, Shuang

doi:10.1098/rsta.2020.0372

Cited by 8 publications

(5 citation statements)

References 45 publications

(68 reference statements)

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“…In [47], the potential of combining semantics and data analysis in the context of DTs is explored, while [48] focuses on the engineering of human-focused systems in the context of Industry 4.0. The application-related articles [49,50] showcase examples of applications in the domains of cyber security and production, respectively. Paper [51] presents an application in the field of health care, specifically in the diagnostics and rehabilitation of multiple sclerosis.…”

Section: Related Workmentioning

confidence: 99%

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Towards a Domain-Neutral Platform for Sustainable Digital Twin Development

Savić,

Segedinac,

Konjović

et al. 2023

Sustainability

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In this paper, we propose an abstract domain-neutral architecture for a cognitive digital twin (CDT) and a software platform to develop such CDTs, including machine reasoning capabilities. Sustainable development refers here to an abstract model that enables a holistic view of limiting resources and has an ability to adapt to different application domains while reusing existing resources. The proposed solution allows for a unified abstract representation and the development of a wide range of diverse digital twins, as well as facilitating their interoperability. The abstract architecture consists of a four-layer structure (observation/actuation layer, data management layer, reasoning layer, and simulation layer) with an upper ontology to which the domain ontology of the specific CDT is mapped. The architecture relies on semantic web technologies, including ontology-based reasoning using OWL, and a loosely coupled, component-based service-oriented software architecture. The platform utilizes a microservice architecture that enables separate, loosely coupled services on each layer, message queues to provide asynchronous communication, and possesses cloud technologies to achieve scalability. The proposed approach was validated by implementing a software platform prototype and demonstrating its key features through two dissimilar scenarios. The first scenario demonstrates simple sustainable energy management through IoT systems inside smart buildings, while the second one demonstrates knowledge quality management based on knowledge space theory.

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Section: Related Workmentioning

confidence: 99%

“…The first approach is a minimized and corrected IITA [69], which is the de facto standard data-driven KS construction algorithm. The second approach is a neuroevolutionary method proposed in [49].…”

Section: Managing Learning Process and Learner's Knowledgementioning

confidence: 99%

Towards a Domain-Neutral Platform for Sustainable Digital Twin Development

Savić,

Segedinac,

Konjović

et al. 2023

Sustainability

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“…2,3 With the communication networks, CPSs are extremely vulnerable to attacks and the security of CPSs has attracted great attention. [4][5][6] Attacks over networks contain denial-of-service (DoS) attacks, 7,8 replay attacks 9,10 and deception/false data injection (FDI) attacks. 11 Among these attacks, deception attacks have the aim of injecting false data in communication with the strong concealment.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, due to the development of communication technology, cyber‐physical systems (CPSs) appear in widespread fields, such as smart electric grids, transportation systems, 1 and unmanned aircraft vehicles (UAVs) 2,3 . With the communication networks, CPSs are extremely vulnerable to attacks and the security of CPSs has attracted great attention 4–6 …”

Section: Introductionmentioning

confidence: 99%

Predictor‐based collision‐free and connectivity‐preserving resilient formation control for multi‐agent systems under sensor deception attacks

Yang,

Shu,

Liu

2024

Intl J Robust & Nonlinear

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Malicious attack is a potential threat to collision‐free and connectivity‐preserving formation. In this article, a predictor‐based collision‐free and connectivity‐preserving resilient formation control strategy is proposed for a class of multi‐agent systems under sensor deception attacks. The predictor states are constructed to replace original states in the control strategy, and a novel attack compensator is constructed to suppress sensor deception attacks. Prediction errors, instead of compromised errors, are introduced to update weights of radial basis function neural networks (RBFNNs). To achieve collision avoidance and connectivity preservation, a transformation function in logarithmic form is introduced. To avoid static and dynamic obstacles, an improved artificial potential function (APF) combined with their velocity information is constructed. Furthermore, to solve the local minimum problem in the combining of transformation function and APF, a virtual force is added. Based on the Lyapunov function, all closed‐loop signals are bounded and collision‐free and connectivity‐preserving formation is achieved. The simulation related to a group of quadrotors verifies the effectiveness of the proposed resilient control strategy.

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“…Furthermore, the authors propose an architectural stochastic MPC-based framework for resilient CPS. The following article is also on cyber-attacks, the authors Liu et al state that such attacks would corrode the sensor and the actuator signals in the cyber layer, and further damage the stability of CPSs [14]. The article proposes an adaptive neural network control for a class of nonlinear CPS subject to false data injection attacks and prescribed constraints.…”

Section: Introductionmentioning

confidence: 99%