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Rapid advances in new-generation information technologies have been the main driving force for the transformation of manufacturing enterprises in Industry 4.0. Digital twin (DT), as a key technology to promote intelligent manufacturing, has shown great potential for manufacturing enterprises to create an industrial intelligence-driven production equipment through in-depth integration of cyber-physical systems. However, the lack of a systematic effective DT modelling method with a supporting evaluation metric is the most important factor restricting the application of DT in manufacturing enterprises. To bridge the gap, this paper proposes a novel multi-level modelling and fidelity evaluation (MLM&FE) method of DT for creating smart production equipment in manufacturing enterprises, which could help enterprises establish an industrial intelligence-driven production environment to quickly respond to changes in the customised global market, thus greatly improving competitiveness of the enterprises. Specifically, this paper firstly designs a reference framework for DT-enhanced smart production equipment, on which an MLM&FE architecture is proposed. Then, key implementation methodologies and tools for MLM&FE are introduced from the perspective of data space modelling, virtual space modelling, knowledge space modelling, model integration and evaluation. Finally, the developed smart production equipment prototype demonstrates the feasibility and effectiveness of DT MLM&FE. [ABSTRACT FROM AUTHOR]

Unlike mass production manufacturing systems, where configurations are rarely changed after the initial design, reconfigurable cyber-physical systems (RCPMS) self-change their structures throughout missions and thus self-adjust production in response to demand requirements. Accordingly, such a paradigm requires enhancing selective maintenance strategy to optimise scheduling maintenance actions, selecting configuration layouts for capacity and product family changes, and achieving maintenance cost reduction and reliability maximisation. This paper is the first to propose a robust model for a selective maintenance problem with imperfect repairs in the RCPMS context. The model also integrates uncertainties originating from the imperfect observations of components' health status. The model's objectives are to maximise the expected reliability and minimise the variance and maintenance cost under maintenance resource constraints. Moreover, we propose a new deep reinforcement learning framework for solving the resulting multi-objective and combinatorial optimisation problem. In addition, we use decision values to enhance the scalarisation process by permitting the priorities of specific objectives to be adjusted after the learning process. Furthermore, we employ Analytical Hierarchy Process to adjust the static priorities with respect to the objective functions and the actual learning context. Finally, broad experiments are conducted to highlight the performance of the proposed model and resolution framework. [ABSTRACT FROM AUTHOR]

Traditional machining is transforming to digital and intelligent machining, in which adaptive machining cyber-physical system (CPS) provides a useful approach to control the machining quality of large thin-walled parts. And the running of adaptive machining CPS is a complex multi-processes execution flow, which can be regarded as a continuous–discrete hybrid system. To realise adaptive controlling of machining quality and adaptive managing of process flow, a unified model for continuous–discrete hybrid adaptive machining CPS is constructed. Firstly, an architecture of adaptive machining CPS is proposed. Next, the cutting process in adaptive machining CPS is modelled as a continuous-variable system (CVS), while the process flow in adaptive machining CPS is modelled as a discrete-events system (DES). Then, the finite state machine is adopted to integrate the CVS and DES to form the unified model of adaptive machining CPS. Finally, an adaptive machining CPS is developed based on the unified model, and the machining results demonstrate that machining quality is efficiently controlled, as well as the process flow is orderly managed. The built unified model has four features, respectively universality, integrability, scalability, and reconfigurability, which can be reconstructed to form a new instancing model according to the different machining requirements. [ABSTRACT FROM AUTHOR]

With the rise of Internet of Things (IoT) technology, the seamless connection between the physical and digital worlds has been realized. This review paper aims to conduct a science mapping review of IoT applications in industrial management and to identify mainstream research topics, research gaps, and future research directions. Using VOSviewer as a visualization tool, 142 articles retrieved from the Scopus database were quantitatively discussed using scientometric analysis. Additionally, a follow-up qualitative discussion was focused on mainstream research topics, existing research gaps, and future research directions as the main research goals. The results revealed influential findings for the co-occurrence of keywords, journals, countries, authors, and documents analyses. Moreover, it was found that the existing research mainly focused on four main research topics including (1) application of IoT in manufacturing based on cyber-physical systems, (2) IoT-related technologies on logistics and supply chain management, (3) The impact of IoT on business models, and (4) Industrial IoT (IIoT) in the context of Industry 4.0. On this basis, the existing research gaps and future research directions are proposed. This review paper would help relevant practitioners and researchers to better understand the existing body of knowledge and lay the foundation for further research. [ABSTRACT FROM AUTHOR]

The availability of cyber-physical systems (CPS) in modern manufacturing networks provides a multitude of interesting opportunities from a manufacturing control perspective. Providing sensors, data gathering, local computation and communication capabilities modern CPS fulfil the technical requirements to act completely autonomously in a manufacturing network. While the distribution of decision-making authority to autonomous entities is feasible given such requirements, practice often sees the monopolisation of decision-making authority for centralised control. However, distributed production control approaches might be better suited given current manufacturing challenges, ranging from unreliable supply chains over highly volatile markets, to the demand for increasingly efficient and highly customisable production. In this article, we extend an existing scheduling complexity framework which enables practitioners and researchers alike to assess the aptitude of given manufacturing networks for both centralised and distributed control. In particular, we study the influence of a manufacturing network's topology ranging from assembly line to job shops on the aforementioned aptitude, with total production costs as objective. We utilise a multi-agent-based discrete-event simulation comparing an MILP-based centralised control approach and an autonomy based distributed control approach with weighted costs as decision function to evaluate this framework. Our results provide novel insights regarding the influence of manufacturing network topologies on the scheduling complexity of manufacturing networks. [ABSTRACT FROM AUTHOR]

The metaverse and Web 3.0 have created a new digital world with specific properties and behaviours replicating and influencing the behaviours and processes of physical entities. This study aims to advance our understanding of how the metaverse will impact supply chain and operations management (SCOM). Using elements of a structured literature search and building on the concepts of cyber-physical systems, digital supply chain twins, cloud supply chains, and Industry 4.0/Industry 5.0, we propose a framework for metaverse SCOM encompassing multiple socio-technological dimensions. We conclude that further metaverse developments could result in a co-existence of physical SCOM, metaverse SCOM, and SCOM for coordination of the physical and metaverse worlds. We offer a structured future research agenda pointing to new research questions and topics stemming from metaverse-driven visibility, computational power for data analytics, digital collaboration, and connectivity. New research areas can emerge for the novel metaverse SCOM processes and decision-making areas (e.g. joint demand forecasting for metaverse and physical products, digital inventory allocation in the metaverse, integrated production planning for the metaverse and physical worlds, and pricing and contracting for digital products), as well as new performance measures (e.g. virtual customer experience level, availability of digital products, and digital resilience and sustainability). [ABSTRACT FROM AUTHOR]

Warehouse operations have been significantly improved because of the rapid advancement of cyber-physical system technologies, preparing for Work-of-the-Future. The challenge, however, is how to design a collaborative system to deliver optimal performance by multiple agents who are highly distributed but interconnected and operate with technologies that provide massive amounts of real-time data. To address the challenge, in this article, the Cyber Collaborative Protocol for Dual-Cycle Task in Future Warehouse is developed to minimise total operation cost and time. The problem is addressed in two phases; the Global and Local phases. The global phase has higher computational power, maintaining a mathematical model, while the local phase has limited computational power and time, utilising heuristics to deliver the outcome. Computer experiments are utilised for validating the designed protocol compared with other alternatives. The results show that, in all given scenarios, the newly designed protocol outperforms alternatives with statistical significance. The original contribution of this research is the design and control of Cyber Collaborative Warehouse operations with a new focus on collaborative multi-agent interactions. In addition, a major implication is that future warehouses can benefit competitively by operating with Task Administration Protocols such as the new Cyber Collaborative Protocol for Dual-Cycle Task in Future Warehouse. [ABSTRACT FROM AUTHOR]

This article investigates the security ensured state estimation problem for cyber‐physical system via a data‐driven method. First, based on the fact that different packets possess varying a degree of significance, that is, some packets play more important roles in the state estimation than others, a novel stochastic important‐data‐based (IDB) attack mechanism is constructed from the attacker's perspective, which can focus on attacking the important packets thus is expected to achieve more destructiveness. Second, as a countermeasure to the proposed IDB attack, a new data‐driven compensation method is proposed, for the first attempt, to compensate for the attack effect and enhance the estimation quality. The designed defense strategy has the following two advantages: (1) only system input and output data are utilized to establish the novel estimator, without knowing the actual system model knowledge, and (2) by constructing a data‐driven output predictor to compensate for the data loss, the accuracy of the state estimation can be efficiently improved. With the aid of the least squares technique and completing square technique, a minimum upper bound matrix for the estimation error covariance is obtained by properly designing the estimator gain. Finally, an illustrative example is given to highlight the destructiveness of the designed stochastic IDB attack and the effectiveness of the proposed novel data‐based compensation method. [ABSTRACT FROM AUTHOR]

In this article, a resilient model predictive control algorithm incorporating a dual‐mode control strategy is proposed for a class of cyber‐physical systems subject to state and input constraints, additive disturbances and denial‐of‐service (DoS) attacks. An adversary aims to disrupt the controller‐to‐actuator channel by deploying adversarial jamming signals. By exploring the prediction uncertainty between a nominal model and an actual model and utilizing a constraint tightening technique, a novel set of constraints is developed, which guarantees robust constraint satisfaction in open‐loop mode caused by DoS attacks. A robust positive invariant set is designed, which allows the control mode to switch from rolling optimization to a state‐feedback control law when the state enters its interior, thus saving computational resources. We explicitly determine a critical duration for DoS attacks and, through theoretical analysis, establish that when the duration of DoS attacks falls below this critical value, the proposed algorithm ensures guaranteed recursive feasibility and robust asymptotic stability. Finally, some comparisons are provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This article investigates the trade‐off between privacy and security in cyber‐physical systems, with the goal of designing a privacy‐preserving mechanism based on information theory. Considering the unreliability of the communication channel, we assume that the private data is vulnerable to eavesdropping and bias injection attacks. To maintain privacy, the system is equipped with a privacy‐preserving mechanism achieved by injecting Gaussian‐type privacy noise into transmitted data, which inevitably leads to degraded detecting performance. Therefore, we investigate the trade‐off between privacy level and detection performance, where the privacy level and the detection performance are measured by mutual information and Kullback–Leibler divergence, respectively. Then, the optimal privacy noise is obtained by solving a convex optimization problem for maximizing the privacy degree and constraining a bound on detection performance degradation. Furthermore, to optimize the detection performance, another convex optimization problem is proposed to minimize both the false alarm rate and the missed alarm rate while guaranteeing a level of the privacy. Finally, a numerical example of the vehicle tracking problem is adopted to illustrate the effectiveness of the designed framework. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an extended version of Deeper, a search‐based simulation‐integrated test solution that generates failure‐revealing test scenarios for testing a deep neural network‐based lane‐keeping system. In the newly proposed version, we utilize a new set of bio‐inspired search algorithms, genetic algorithm (GA), (μ+λ) and (μ,λ) evolution strategies (ES), and particle swarm optimization (PSO), that leverage a quality population seed and domain‐specific crossover and mutation operations tailored for the presentation model used for modeling the test scenarios. In order to demonstrate the capabilities of the new test generators within Deeper, we carry out an empirical evaluation and comparison with regard to the results of five participating tools in the cyber‐physical systems testing competition at SBST 2021. Our evaluation shows the newly proposed test generators in Deeper not only represent a considerable improvement on the previous version but also prove to be effective and efficient in provoking a considerable number of diverse failure‐revealing test scenarios for testing an ML‐driven lane‐keeping system. They can trigger several failures while promoting test scenario diversity, under a limited test time budget, high target failure severity, and strict speed limit constraints. [ABSTRACT FROM AUTHOR]

The medical cyber-physical system utilizes various Internet of Medical Things (IoMT) devices that are connected to the network for real-time management and medication of patient. The resource constraint IoMT devices require energy-efficient lightweight security schemes to protect medical data. To the best of our knowledge, majority of the existing studies concentrate either on security or on energy efficiency issues. The main contribution of this research is to develop an energy-efficient lightweight authentication method without sacrificing the security level. The authentication is done using multiple factors, namely password, physical unclonable function (PUF), Chebyshev polynomial, smartcard and fuzzy extractor. Chebyshev polynomial is used for non-identification of the private key by any attacker, whereas PUF protects the smart card from cloning by generating unique challenge–response pairs. Apart from authentication, the proposed scheme includes Blockchain-enabled distributed trustable ecosystem among independent participants where miner is selected using lightweight fuzzy system. The proposed scheme carries out the formal security analysis using real or random model which is perseverance against different external attacks and the security verification has been done using AVISPA (Automate Validation of Internet Security Protocols and Applications) tool. As a major finding, the simulation result using NS-3 simulator confirms that the proposed study outperforms the existing studies in terms of packet loss rate, throughput, end-to-end delay, computational cost and communication cost. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of developing a unified method for quantifying and improving the cyber resilience of large-scale cyber-physical systems (CPSs) against cyber-attacks targeting structural controllability. To this end, we provide necessary and sufficient conditions to extend the concept of structural controllability of single-layer systems to two-layer CPSs comprising of power network as the physical layer and communication network as the cyber layer. Next, in order to assess cyber resilience, we introduce the notion of $ R $ R -resilient CPS as the maximum number of cyber nodes that can be attacked by an adversary while preserving the structural controllability of the entire CPS. Furthermore, we provide an algorithm to find the optimal network reconfiguration matrix to increase the resilience level of CPS from $ R $ R -resilient to $ ({R + 1}) $ (R + 1) -resilient by adding a minimum number of transmission lines to the physical layer or communication links to the cyber layer, or both. Additionally, the proposed methodology is extended to achieve $ ({R + \alpha }) $ (R + α) -resilient CPS, where α>1. The effectiveness of the proposed method is verified through simulations of IEEE 14, IEEE 39, and IEEE 118 power networks with different cyber topologies, considering four scenarios of denial-of-service (DoS) attacks, including targeted and random node attacks. [ABSTRACT FROM AUTHOR]

To address the security of cyber-physical systems, stealthy attacks, a class of false data injection attacks that can impact a system without being detected, have been studied in the recent decade. The existing discussions on stealthy attack design have rarely considered systems with switching structures, but recent literature shows the importance of this problem. Therefore, we are motivated to investigate a stealthy man-in-the-middle attack strategy that can be applied to switched systems. Specifically, we first investigate how to design a stealthy attack without using mode information. Then, we consider whether the attacker can infer the mode information that can be used to design a stealthy attack. By combining mode identification and stealthy attack design, a stealthy man-in-the-middle attack strategy for switched systems is proposed. In addition, the feasibility and effectiveness of the strategy are discussed, and an illustrative numerical example is given to demonstrate the proposed strategy. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of tracking target control of a nonlinear system in the context of cyber-physical systems for connected autonomous vehicles with external unknown disturbances. A new robust tracking strategy via backstepping sliding-mode control is proposed. First, a connected nonlinear vehicle dynamical model with disturbances is constructed. Then, a disturbance observer is presented to approximate the unknown disturbances when the derivative of the disturbance is bounded. This paper has proved that the error of the observation can converge to zero in finite time. Third, a tracking control method is designed which combines the backstepping method with the sliding-mode method. According to the method, the estimated values of interference are used as a priori knowledge. Furthermore, the stability of the designed control strategy is demonstrated through using the Lyapunov theory. Finally, simulation experiments are presented to demonstrate the feasibility of the proposed approaches. [ABSTRACT FROM AUTHOR]

This article examines resource scheduling optimization for cyber‐physical systems that include a smart sensor, a remote estimator, and an attacker. The attacker's objective is to use denial‐of‐service (DoS) attack to jam the wireless channel, causing packet loss and reducing system performance. We consider a more realistic scenario where other interfering sources exist and conduct research based on the signal‐to‐interference‐plus‐noise ratio (SINR) model. Through this model, we investigate two cases: constant high or low interference power and time‐varying interference power. Different levels of attack power lead to varying packet loss rates, and the same attack power may result in different packet loss rates in different interference environments. Therefore, the attacker needs to consider the impact of other interferences to find the optimal attack strategy that increases the estimation error of the information‐physical system while reducing energy consumption. We describe this optimization problem in a Markov decision process (MDP) framework and demonstrate the existence of an optimal deterministic Markovian strategy. We also establish the monotonicity of the optimal strategy in two different cases and investigate the extreme case. Simulation results support our theoretical results. [ABSTRACT FROM AUTHOR]

Purpose: Additive manufacturing also known as 3D printing is an evolving advanced manufacturing technology critical for the new era of complex machinery and operating systems. Manufacturing systems are increasingly faced with risk of attacks not only by traditional malicious actors such as hackers and cyber-criminals but also by some competitors and organizations engaged in corporate espionage. This paper aims to elaborate a plausible risk practice of designing and demonstrate a case study for the compromised-based malicious for polymer 3D printing system. Design/methodology/approach: This study assumes conditions when a machine was compromised and evaluates the effect of post compromised attack by studying its effects on tensile dog bone specimens as the printed object. The designed algorithm removed predetermined specific number of layers from the tensile samples. The samples were visually identical in terms of external physical dimensions even after removal of the layers. Samples were examined nondestructively for density. Additionally, destructive uniaxial tensile tests were carried out on the modified samples and compared to the unmodified sample as a control for various mechanical properties. It is worth noting that the current approach was adapted for illustrating the impact of cyber altercations on properties of additively produced parts in a quantitative manner. It concurrently pointed towards the vulnerabilities of advanced manufacturing systems and a need for designing robust mitigation/defense mechanism against the cyber altercations. Findings: Density, Young's modulus and maximum strength steadily decreased with an increase in the number of missing layers, whereas a no clear trend was observed in the case of % elongation. Post tensile test observations of the sample cross-sections confirmed the successful removal of the layers from the samples by the designed method. As a result, the current work presented a cyber-attack model and its quantitative implications on the mechanical properties of 3D printed objects. Originality/value: To the best of the authors' knowledge, this is the original work from the team. It is currently not under consideration for publication in any other avenue. The paper provides quantitative approach of realizing impact of cyber intrusions on deteriorated performance of additively manufactured products. It also enlists important intrusion mechanisms relevant to additive manufacturing. [ABSTRACT FROM AUTHOR]

The impact of communication disturbances on microgrids (MGs) needs robust and scalable Information Communication Technology (ICT) infrastructure for efficient MG control. This work builds on advances in the Internet of Things (IoT) to provide a practical platform for testing the impact of various cyber-attacks on a distributed control scheme for a Multi-Agent System (MAS). This paper presents a Controller Hardware-in-the-Loop (CHIL) testbed to investigate the impact of various cyber-attacks and communication disruptions on MGs. A distributed consensus secondary control scheme for a MAS within an MG cyber-physical system (CPS) is proposed. The proposed cyber-physical testbed integrates a real-time islanded AC microgrid on RT-Lab, secondary controllers implemented on single-board computers, and an attacker agent on another single-board computer. Communication occurs via a UDP/IP network between OPAL-RT and controller agents, as well as between the agents. Through meticulous experimentation, the efficacy of the proposed control strategy using the developed platform is validated. Various attacks were modeled and launched including deception attacks on sensors, actuators, and their combinations, as well as disruption attacks. The ramifications of both deception and disruption cyber-attacks on system performance are analyzed. [ABSTRACT FROM AUTHOR]

The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems, with a focus on cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in offline and online process optimization approaches are presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, reduced energy consumption, and carbon emissions are independently investigated for these offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, this paper explores the challenges and opportunities inherent to developing a robust cyber–physical optimization system. [ABSTRACT FROM AUTHOR]

The escalating intricacy of industrial systems necessitates strategies for augmenting the reliability and efficiency of industrial machinery to curtail downtime. In such a context, predictive maintenance (PdM) has surfaced as a pivotal strategy. The amalgamation of cyber-physical systems, IoT devices, and real-time data analytics, emblematic of Industry 4.0, proffers novel avenues to refine maintenance of production equipment from both technical and managerial standpoints, serving as a supportive technology to enhance the precision and efficacy of predictive maintenance. This paper presents an innovative approach that melds text mining techniques with the cyber-physical infrastructure of a manufacturing sector. The aim is to improve the precision and promptness of predictive maintenance within industrial settings. The text mining framework is designed to sift through extensive log files containing data on the status of operational parameters. These datasets encompass information generated by sensors or computed by the control system throughout the production process execution. The algorithm aids in forecasting potential equipment failures, thereby curtailing maintenance costs and fortifying overall system resilience. Furthermore, we substantiate the efficacy of our approach through a case study involving a real-world industrial machine. This research contributes to the progression of predictive maintenance strategies by leveraging the wealth of textual information available within industrial environments, ultimately bolstering equipment reliability and operational efficiency. [ABSTRACT FROM AUTHOR]

System knowledge and reasoning mechanisms are essential means for intellectualization of cyber-physical systems (CPSs). As enablers of system intelligence, they make such systems able to solve application problems and to maintain their efficient operation. Normally, system intelligence has a human-created initial part and a system-produced (extending) part, called synthetic system intelligence (SSI). This position paper claims that SSI can be converted to a new industrial asset and utilized as such. Unfortunately, no overall theory of SSI exists and its conceptual framework, management strategy, and computational methodologies are still in a premature stage. This is the main reason why no significant progress has been achieved in this field, contrary to the latent potentials. This paper intends to contribute to: (i) understanding the nature and fundamentals of SSI, (ii) systematizing the elicitation and transfer of SSI, (iii) exploration of analogical approaches to utilization of SSI, and (iv) road-mapping and scenario development for the exploitation of SSI as an industrial asset. First, the state of the art is surveyed and the major findings are presented. Then, four families of analogical approaches to SSI transfer are analyzed. These are: (i) knowledge transfer based on repositories, (ii) transfer among agents, (iii) transfer of learning resources, and (iv) transfer by emerging approaches. A procedural framework is proposed that identifies the generic functionalities needed for a quasi-autonomous handling of SSI as an industrial asset. The last section casts light on some important open issues and necessary follow-up research and development activities. [ABSTRACT FROM AUTHOR]

Summary: The underlying intention of this work is to devise a tracking control protocol for the nonlinear cyber‐physical systems that are prone to external disturbances, time‐varying input delays and deception attacks. To be more precise, the modified repetitive tracking controller is formulated for ensuring the asymptotic tracking outcomes of the assayed system with the aim of addressing the impacts caused by external disturbances as well as input time‐varying delays. First of all, the nonlinearities of the investigated cyber‐physical systems are efficaciously approximated by the interval type‐2 fuzzy model approach. Further, the predictor technique is utilized in this study to mitigate the influence of input delays and specifically, the extended Smith predictor approach is employed. That is, the transfer function is inserted into the conventional Smith predictor's main feedback channel. In parallel, the active disturbance rejection technique is executed to reject and estimate the external disturbance impacts on the assayed system. Notably, the improved equivalent‐input‐disturbance estimator approach is tied up and more precisely, the estimated disturbances obtained from the afore‐said estimator are incorporated into the proposed controller. Whilst the deception attacks are characterized as a stochastic distributed random variable that are governed by the Bernoulli distribution. With all of this as a backdrop, the appropriate delay‐dependent constraints are expressed in the context of linear matrix inequalities by employing the Lyapunov stability theory. In line with the predetermined criteria, the relations for computing the required gain matrices are also formulated. Ultimately, the simulation analysis is carried out using two distinct numerical examples to confirm the importance of the proposed control mechanism from the perspective of theory as well as practice. [ABSTRACT FROM AUTHOR]

High-performance computing (HPC) processors are nowadays integrated cyber-physical systems demanding complex and high-bandwidth closed-loop power and thermal control strategies. To efficiently satisfy real-time multi-input multi-output (MIMO) optimal power requirements, high-end processors integrate an on-die power controller system (PCS). While traditional PCSs are based on a simple microcontroller (MCU)-class core, more scalable and flexible PCS architectures are required to support advanced MIMO control algorithms for managing the ever-increasing number of cores, power states, and process, voltage, and temperature variability. This paper presents ControlPULP, an open-source, HW/SW RISC-V parallel PCS platform consisting of a single-core MCU with fast interrupt handling coupled with a scalable multi-core programmable cluster accelerator and a specialized DMA engine for the parallel acceleration of real-time power management policies. ControlPULP relies on FreeRTOS to schedule a reactive power control firmware (PCF) application layer. We demonstrate ControlPULP in a power management use-case targeting a next-generation 72-core HPC processor. We first show that the multi-core cluster accelerates the PCF, achieving 4.9x speedup compared to single-core execution, enabling more advanced power management algorithms within the control hyper-period at a shallow area overhead, about 0.1% the area of a modern HPC CPU die. We then assess the PCS and PCF by designing an FPGA-based, closed-loop emulation framework that leverages the heterogeneous SoCs paradigm, achieving DVFS tracking with a mean deviation within 3% the plant's thermal design power (TDP) against a software-equivalent model-in-the-loop approach. Finally, we show that the proposed PCF compares favorably with an industry-grade control algorithm under computational-intensive workloads. [ABSTRACT FROM AUTHOR]

Various studies have demonstrated that the collaborative false command injection (FCI) and false sensory data injection (FDI) attacks can go undetected by the existing detection methods and result in serious cascading failures. Therefore, fast attack-recovery under attacks are gaining importance. However, previous researches cannot know which data are contaminated and real system state cannot be evaluated, which leads to serious misalignment of attack-recovery. In this paper, we propose a novel and effective method based on physical invariants among heterogeneous control commands and sensory data to locate the maliciously modified command or compromised sensor. First, we analyze changes in invariants under different single attacks and depict how to detect the collaborative attack and locate attack targets by utilizing broken invariants. Second, considering localization becomes difficult when multiple attacks may be launched simultaneously to disrupt different components, we build the causal network by utilizing the physical invariants and develop a causal-network-based algorithm to fast locate compromised objects. Finally, our numerical results validate the effectiveness of our proposed methods and algorithms. Our work will build the foundation to achieve real-time and accurate recovery under attacks. [ABSTRACT FROM AUTHOR]

This paper studies a secure iterative interval estimation approach for cyber-physical systems subject to stealthy deception attacks. Under the hypothesis that the system is accessed by a stealthy attack, an iteration scheme integrating the T-N-L observer framework is employed to reconstruct the system state. With the help of a structure separation method, a sufficient condition in terms of linear matrix inequality is provided to obtain convergent observation errors under deception attacks. Resorting to the reachability analysis, a secure state interval is built by means of the analyzed attack bounds and the observation error interval. Simulation studies verify the effectiveness of the proposed method for attack and attack-free cases. [ABSTRACT FROM AUTHOR]

Cooperative Vehicular Platooning (Co-VP) is a prime example of Cooperative Cyber-Physical Systems (Co-CPS), offering great potential for enhancing road safety by reducing human involvement in driving. However, this domain presents significant challenges, incorporating control theory, communications, vehicle dynamics, security, and traffic engineering. This survey explores recent advancements in Co-VP, covering control strategies, communication infrastructures, and cybersecurity. It also examines testing and validation methods, such as simulation tools, hardware-in-the-loop setups, and vehicular testbeds. Lastly, it outlines open challenges within the Co-VP field. This comprehensive overview serves as a guide for further developments in this complex and critical area. [ABSTRACT FROM AUTHOR]

This paper investigates the secure state estimation problem of the cyber-physical systems (CPSs) in the presence of malicious attacks. Considering the destructiveness and stealthiness of the attacker, a novel stochastic transmission scheme, in which the repeater generates transmission data by randomly combining some measurement values, is proposed to ensure the effectiveness of the attack detector. A filter equipped with the $ \chi ^2 $ χ 2 detector and the stochastic transmission scheme is proposed to conduct the secure state estimation. The attack probability condition of the estimation error being mean-square bounded is given, which indicates that stochastic transmission scheme has a limited effect on the filter. Some numerical simulations are given to illustrate the effectiveness of the proposed transmission scheme. [ABSTRACT FROM AUTHOR]

Connected electronic devices used in healthcare, such as small sensors and actuators and other cyber-physical devices, make up what is known as the Internet of Medical Things. By linking these devices, medical monitoring, analysis, and reporting may become more intelligent and autonomous, ultimately benefiting the patient. To solve these problems, a thorough plan for monitoring and assessing method health needs to be formulated. The application of traditional machine learning to massive datasets in a shared computing environment may lead to model training and computation that is either too slow to provide accurate results or too fast to produce erroneous results as a consequence of rushed training, both of which are detrimental. Key 6G enablers may be seen of as the upcoming paradigms of machine learning (ML), quantum computing, and quantum ML (QML), as well as their synergies with communication networks. This research presents a new machine learning approach to medical cyber physical system (CPS) predictive maintenance using swarm robots. In this case, we use fog computing-integrated soft sensors to conduct a thorough closed-loop dynamic analysis of the CPS predictive maintenance. Extreme learning utilising a weighted sliding window. The investigation of medical security is then conducted using a kernel decision making system based on probabilistic swarm robotics. In terms of training accuracy, energy use, convergence ratio, precision, and F_measure, an experimental study is conducted. The system's ability to (i) avoid unscheduled downtime and (ii) consequently increase operational availability sets it apart from other systems. The system aims to notify users as quickly as feasible of unusual operations and probable failure scenarios. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative adaptive nonsingular Second-Order Terminal Sliding Mode (SOTSM) control strategy specifically designed to stabilise disturbed nonlinear Cyber-Physical Systems (CPSs) within a finite timeframe. Unlike the conventional methods, our approach incorporates a novel nonlinear sliding surface that eliminates the need for a reaching step, significantly enhancing the system's robustness. Novel real-time adaptive control laws are developed to cope with external perturbations, time-varying delays, and cyber-attack on actuators without requiring the identification of their upper bounds. The proposed method ensures robust performance for time-varying delayed and disturbed nonlinear CPSs, even in the face of strong cyber-attacks targeting the actuators. Moreover, the proposed method has distinct advantages: it delivers rapid response times, enhanced flexibility, exceptional accuracy, smooth and robust control devoid of transient fluctuations or chattering, and ensures finite-time convergence. Simulation results comprehensively demonstrate the superior efficiency and success of our proposed technique compared to traditional methods such as integral Sliding Mode Control (SMC) and State-Feedback Control (SFC) schemes. [ABSTRACT FROM AUTHOR]

Malicious attacks are often inevitable in cyber-physical systems (CPS). Accuracy in Cyber physical system for position tracking of servos is the major concern now a days. In high precision industrial automation, it is very hard to achieve accuracy in tracking especially under malicious cyber-attacks, control saturations, parametric perturbations and external disturbances. In this paper, we have designed a novel predefined time (PDT) convergence sliding mode adaptive controller (PTCSMAC) for such kind of cyber physical control system. Main key feature of our control is to cope these challenges that are posed by CPS systems such as parameter perturbation, control saturation, and cyber-attacks and the whole system then upgrade to a third-order system to facilitate adaptive control law. Then, we present an adaptive controller based on the novel PDT convergent sliding mode surface (SMS) combined with a modified weight updated Extreme Learning Machine (ELM) which is used to approximate the uncertain part of the system. Another significant advantage of our proposed control approach is that it does not require detailed model information, guaranteeing robust performance even when the system model is uncertain. Additionally, our proposed PTCSMAC controller is nonsingular regardless of initial conditions, and is capable of eradicating the possibility of singularity problems, which are frequently a concern in numerous CPS control systems. Finally, we have verified our designed PTCSMAC control law through rigorous simulations on CPS seeker servo positioning system and compared the robustness and performance of different existing techniques. [ABSTRACT FROM AUTHOR]

The main focus of this article is to explore the finite‐time stabilization of time‐varying cyber‐physical systems (CPS) that are operating over multipath networks and under random denial‐of‐service (DoS) attacks. In contrast to prior studies, we present a set of necessary and sufficient conditions for achieving finite‐time stability in the closed‐loop model of CPS operating over single‐path network. Notably, this equivalent conditions are established based on the analysis of the state transition matrix (STM). Additionally, we utilized auxiliary systems to study more general CPS operating over multipath networks and obtained a sufficient condition based on STM. Finally, we address a sufficient condition based on the Lyapunov function and introduce a linear matrix inequality (LMI)‐based approach that facilitates the computation of nonfragile controllers. The effectiveness of the developed theoretical results is verified through a stock investment model. [ABSTRACT FROM AUTHOR]

Due to the close interaction between computing, sensing, communication, and driving of the cyber‐physical system (CPS), which makes it highly vulnerable to cyber attacks, the security of CPS has attracted wide attention. In the article, we discuss the system identification problem of the finite impulse response system under sequence denial of service attacks. The identification algorithm of the unknown parameter is designed under binary observations, and its convergence performance is analyzed. The optimal attack strategy problem is modeled as a optimized problem with constraints based on the covariance matrix of estimation error, the solution method and implementation algorithm are given. From the perspective of the defender, an encryption‐type defense algorithm is designed, and then the optimal defense strategy is presented. The numerical simulation is used to verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a prescribed performance‐based model‐free adaptive resilient control (MFARC) algorithm to address the realization of trajectory tracking for nonlinear cyber‐physical systems (CPSs) under aperiodic denial‐of‐service (DoS) attacks. Firstly, an equivalent linear model is constructed by utilizing the dynamic linearization technique, and a novel output transformation approach is applied to convert the constrained system to an unconstrained one for the MFARC framework. Then, an attack compensation mechanism is presented to estimate the unavailable output signal when attacks are active, such that the negative impact of aperiodic DoS attacks can be compensated. Based on the unconstrained model and the compensation mechanism, a prescribed performance‐based MFARC strategy is constructed to steer the tracking error to the predetermined neighborhood around the origin in the presence of DoS attacks. A major improvement of our result over previous studies is the introduction of prescribed performance control (PPC) into MFARC while reasonably restricting the transient and steady‐state performance of the tracking error even when DoS attacks occur. Additionally, we utilize the linear matrix inequality (LMI) toolbox to obtain better tracking performance by suitably adjusting the time‐varying parameters, which is superior in practical applications. Ultimately, our results indicate that the tracking error consistently remains within a predefined bound. [ABSTRACT FROM AUTHOR]

This paper studies the state estimation for cyber‐physical systems with multiple channels under joint Denial‐of‐Service and deception attacks. We show that the optimal estimator (OE) cannot be implemented in practice, as its computational complexity exponentially grows with respect to time. Then, we develop a centralized approximate optimal estimator (AOE) by using a generalized pseudo‐Bayesian algorithm. This AOE is not only computationally efficient, but also features theoretically guaranteed performance and stability. Specifically, it is proved when the attack success rate is less than a threshold value, both the OE and the AOE are stable, and the averaged performance deviation between the OE and the proposed AOE remains bounded. Finally, simulation examples are provided to illustrate the obtained results. [ABSTRACT FROM AUTHOR]

By fully taking the effects of data injection attacks and nonlinear disturbances into consideration, based on dynamic asynchronous event‐triggered approach, an adaptive robust H‐Infinity switching filtering (ARSF) method is proposed for the stability of industrial cyber physical systems. An adaptive feedback controller utilizing anticipated states and error‐dependent parameters can proficiently counteract the deleterious impacts of data injection attacks originating from the measurement channel. Furthermore, a H∞$$ {H}_{\infty } $$ filter, which includes the value of the measurement signal, is designed to monitor the system's state changes in real‐time and ensure the stable operation of the system under attacks. Based on the design of an adaptive feedback controller and a H∞$$ {H}_{\infty } $$ filter eliminating the effects of data injection attacks originating from the measurement channel, a switching scheme is further proposed for compensating the effect of data injection attacks in the control channel. Finally, simulation tests are conducted by using MATLAB to control the robustness of a practical quadrotor UAV system's angles by adopting ARSF, whose results verify that the correctness and effectiveness of the proposed ARSF. [ABSTRACT FROM AUTHOR]

In recent years, with rising urbanization and ongoing adjustments in industrial structures, there has been a growing dependence on public buildings. The load of public buildings gradually becomes the main component of the peak load in summer, among which the load of air conditioning is particularly prominent. To clarify the key problems and solutions to these challenges, this study proposes a multi-objective optimization control strategy for building air conditioning cluster participation in demand response based on Cyber-Physical System (CPS) architecture. In a three-layer typical CPS architecture, the unit level of the CPS achieves dynamic information perception of air conditioning clusters through smart energy terminals. An air conditioning load model based on the multiple parameter types of air conditioning compressors is presented. Then, the system level of the CPS fuses multiple pieces of information through smart energy gateways, analyzing the potential for air conditioning clusters when they participate in demand response. The system of system level (SoS level) of the CPS deploys a multi-objective optimization control strategy which includes the uncertainty of the initial states of air conditioning clusters within the intelligent building energy management system. The optimal model takes into account the differences in the environmental conditions of each individual air conditioning unit within the cluster and sets different operating modes for each unit to achieve load reduction while maintaining temperatures within a comfortable range for the human body. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm based on Pareto frontiers is applied to solve this optimization control strategy and to optimize the operational parameters of the air conditioning clusters. A comparative analysis is conducted with single-objective optimization results obtained using the traditional Particle Swarm Optimization (PSO) algorithm. The case study results indicate that the proposed multi-objective optimization control strategy can effectively improve the thermal comfort of the human body towards the controlled temperatures of air conditioning clusters while meeting the accuracy of demand response. In the solution phase, the highest temperature within the air conditioning clusters is 24 °C and the lowest temperature is 23 °C. Adopting the proposed multi-objective optimization control strategy, the highest temperature is 26 °C and the lowest temperature is 23.5 °C within the clusters and the accuracy of demand response is up to 92%. Compared to the traditional PSO algorithm, the MOPSO algorithm has advantages in convergence and optimization precision for solving the proposed multi-objective optimization control strategy. [ABSTRACT FROM AUTHOR]

This paper proposes a new secure distributed estimation for cyber‐physical systems against adversarial attacks with noisy input. To mitigate the effect of attacks, a novel distributed attack detection based on a reliable reference estimation obtained by temporal convex combination is proposed. Furthermore, for more effective and robust performance, an adaptation rule to adjust convex combination weights is presented, in which the generalized correntropy method with nonlinear loss function and stochastic gradient descent are utilized. Besides, to eliminate input noise, a bias‐compensation method in local adaptation of the secure distributed estimation is proposed. Simulations show superior dynamic and real‐time adaptability of the proposed algorithm under complex attacking scenarios. [ABSTRACT FROM AUTHOR]

This article addresses the issue of event‐triggered security control for nonlinear cyber‐physical systems, characterized by Denial‐of‐Service (DoS) attacks, time‐varying delays, and actuator faults. These complexities are captured using the Takagi–Sugeno fuzzy model. Significantly, this study focuses on DoS attacks targeting the controller‐to‐actuator channel, and subsequently introduces a model for actuator faults that considers the prolonged utilization or degradation of component parts. To optimize communication resource allocation, a model‐independent event‐triggered mechanism is proposed. Utilizing the Lyapunov–Krasovskii function approach, the objective of achieving stochastic stability is transformed into a linear matrix inequalities problem, thus deriving a set of sufficient conditions. The efficiency and validity of the proposed algorithms is demonstrated through simulation results. [ABSTRACT FROM AUTHOR]

In this article, an adaptive security control scheme is proposed for a class of nonlinear time‐varying full‐state constrained cyber‐physical system with false data injection attacks and actuator failures. Aiming at the problem of sensor network attack, the influence of sensor network attack is reduced by backstepping technique and Nussbaum function. In the control design process, the time‐varying barrier Lyapunov function is used to constrain the system states. In addition, adaptive laws are designed to eliminate the influence of actuator faults (effectiveness loss and bias fault). And the developed controller ensures that the state constraints will not be violated when sensor attacks and actuator faults occur simultaneously. Finally, the simulation result verify the effectiveness of the scheme. [ABSTRACT FROM AUTHOR]