Control Of Cyber-physical Systems Research Articles

Resilient Self-Triggered Model Predictive Control of Cyber-Physical Systems Under Two-Channel False Data Injection Attacks

Abstract This paper presents a novel resilient self-triggered model predictive control (ST-MPC) method to alleviate potential threats of false data injection (FDI) attacks on cyber-physical systems (CPSs). First, considering that the data transmitted via the sensor-to-controller (S–C) and controller-to-actuator (C–A) channels in CPS may be tampered with by FDI attacks, a novel input reconstruction strategy combined with the ST-MPC mechanism is proposed to alleviate the threats of FDI attacks while reducing the computational and communication resources, in which key optimal control signals are selected and protected based on systematic performance inequalities. Correspondingly, a resilient ST-MPC algorithm combined with the dual-mode strategy is further proposed. Moreover, the iterative feasibility and the closed-loop stability are strictly demonstrated. Finally, the effectiveness of the proposed strategy is verified via a simulation study.

Prescribed-Time Output Feedback Control for Cyber-Physical Systems Under Output Constraints and Malicious Attacks.

This article presents a prescribed-time output feedback control (PTOFC) algorithm for cyber-physical systems (CPSs) under output constraint occurring in any finite time interval (OC-AFT) and malicious attacks. The OC-AFT meaning that the output constraint only occurs during a finite number of time periods while being absent in others, which is more general and complex than traditional infinite-time/deferred output constraints. A stretch model-based nonlinear mapping function is constructed to handle the OC-AFT, and a salient advantage is that the proposed algorithm is also suit for CPSs with infinite-time/deferred output (or funnel) constraints, as well as those that are constraint-free, without necessitating changes to the control structure. The uncertain terms (including system model uncertainties, malicious attacks, and external disturbances) are compensated by fuzzy logic systems. Furthermore, a novel practical prescribed-time stability criterion is proposed, under which a novel PTOFC scheme is given. The results demonstrate that the proposed scheme can ensure that both tracking error and observation error converge to a neighborhood centered on zero within a prescribed time, while accommodating the OC-AFT and malicious attacks. Additionally, the settling time remains unaffected by control parameters and initial states, and the limitations of excessive initial control inputs and singularity problems in existing prescribed-time control algorithms are eliminated. The developed algorithm is exemplified through simulation instances.

Event-based secure control for cyber-physical systems against false data injection attacks

This paper mainly studies an event-based secure control problem for cyber-physical systems against false data injection (FDI) attacks. First, a dynamic event-triggered transmission protocol is established to schedule the transmitted data from the sensor to the controller. Subsequently, an imperfect FDI attack model is constructed considering the real network environment, which would bypass the traditional residual-based detection methods. To overcome this drawback, an attack detection strategy based on secure history information is designed, which effectively mitigates the negative impact of uncertainty on the detection results. Based on the detection results, an event-based secure linear quadratic Gaussian control scheme is designed, which can guarantee system performance and reduce the communication load effectively. Finally, a simulation experiment verifies that the proposed control method outperforms other algorithms in the literature, and the detection accuracy is increased by 91% to 98%.

Security Control of Cyber-Physical Systems under Cyber Attacks: A Survey.

Cyber-physical systems (CPSs), which combine computer science, control systems, and physical elements, have become essential in modern industrial and societal contexts. However, their extensive integration presents increasing security challenges, particularly due to recurring cyber attacks. Therefore, it is crucial to explore CPS security control. In this review, we systematically examine the prevalent cyber attacks affecting CPSs, such as denial of service, false data injection, and replay attacks, explaining their impacts on CPSs' operation and integrity, as well as summarizing classic attack detection methods. Regarding CPSs' security control approaches, we comprehensively outline protective strategies and technologies, including event-triggered control, switching control, predictive control, and optimal control. These approaches aim to effectively counter various cyber threats and strengthen CPSs' security and resilience. Lastly, we anticipate future advancements in CPS security control, envisioning strategies to address emerging cyber risks and innovations in intelligent security control techniques.

Efficient data-driven event-triggered predictive control for cyber-physical systems

As a multi-dimensional complex system that integrates computing, network, and physical environments, cyber-physical system (CPS) involves a large amount of system data in its real-time control process. The efficiency of data processing and transmission in CPS plays an important role in its control performance, however, few results have addressed such problems. This paper proposes a novel data-driven event-trigger mechanism to alleviate the data processing and transmission load for the data-driven predictive control method in CPS. The design of the event-triggered law can be transformed into solving a linear matrix inequality (LMI) based on the measured system data, while ensuring the stability of the integrated system. The improved performances are illustrated by the explicit simulation results, which indicates its potential application in CPS.

An adaptive predefined time sliding mode control for uncertain nonlinear cyber-physical servo system under cyber attacks

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.

H∞ control for cyber physical systems under DoS attacks via tracking-removed autoencoder

In this paper, we consider cyber physical systems security control problems under DoS attacks via the tracking-removed autoencoder. The DoS attack model is described by the switch signal, which occurs in the controller-actuator transmission channel. Then, the linear matrix inequality theory is used to solve the average dwell time of the attack mode that the system can tolerate. Moreover, when the attack dwell time exceeds the tolerance range, a tracking-removed autoencoder is chosen to train to get the control signal. And it can be proved that the closed-loop system with the controller based on the tracking-removed autoencoder is asymptotically stable with H ∞ performance index. Finally, the correctness and advancement of the theory are proved by an example.

Adaptive control for cyber-physical systems under man-in-the-middle attacks with false data injections

A cyber–physical system (CPS) is often vulnerable to cyber-attacks that can cause immense damage in real-time to the data and operation of the physical system. To prevent CPS from entering an unsafe state of operation due to Man-in-the-Middle (MitM) attack, an adaptive control strategy based on a novel backstepping framework is proposed that is different from the classical n-step predictor scheme. The proposed scheme is computationally faster owing to the fact that the scheme is independent of the design of virtual control laws (VCLs) and also has the capability to handle uncertain system dynamics thereby safeguarding the CPSs from MitM attack. The overall control scheme is proved to be stable in the sense of exponential mean square stability. The article discusses computational complexity analysis of the overall closed-loop control. Moreover, to handle the uncertainties arising from False-Data Injection (FDI), a Radial Basis Function Neural Network (RBFNN), initialized by a Sparrow Search Algorithm (SSA), is employed. The efficacy of the proposed neuro-adaptive control scheme is validated by considering a nonlinear model of the DC motor and comparing the results of the proposed scheme with some existing results.

Review of methods for modeling and control of cyber-physical systems in multi-energy microgrids

The article analyzes the development of methods for modeling and control of multi-energy microgrids through cyber-physical systems. We used the methods of literature review and meta-analysis based on publications from international databases Scopus and Web of Science, Russian database eLibrary, digital platform IEEEXplore et al. According to the analysis, Smart Grid implementation drives the development of cyber-physical systems. As summarized in this study, control interfaces, data transmission channels, and remote debugging ports are vulnerable parts of IoT devices that can possibly be attacked by intruders. A review of the recent publications in this field finds multi-agent technologies to be an effective approach not only for the operational control of multi-energy microgrid modes, but also for the construction of its reliable information network at the level of medium and low voltage systems. In the field of distributed energy systems, literature review of information technology indicates that the more capabilities are added to receive and process various kinds of information (transaction data, mode parameters, status of controllers, etc.) from external sources, the more vulnerable a multi-energy microgrid is to any cyber threats. Modern mathematical methods such as artificial intelligence, dynamic optimization, and multi-agent approaches should be used to effectively solve the problem of load distribution between different energy sources with cost minimization.

Adversarial Inference Control in Cyber-Physical Systems: A Bayesian Approach With Application to Smart Meters

Adversarial Inference Control in Cyber-Physical Systems: A Bayesian Approach With Application to Smart Meters

Exponential mean-square stabilization control for cyber-physical systems under random DoS attacks and transmission delay

Exponential mean-square stabilization control for cyber-physical systems under random DoS attacks and transmission delay

Data-Driven Dynamic Periodic Event-Triggered Control of Cyber-Physical Systems Under Packet Dropouts and DoS Attacks

Data-Driven Dynamic Periodic Event-Triggered Control of Cyber-Physical Systems Under Packet Dropouts and DoS Attacks

Event-based networked predictive control of cyber-physical systems with delays and DoS attacks

Event-based networked predictive control of cyber-physical systems with delays and DoS attacks

Performance-based event-triggered observer design for security control of cyber-physical systems with malice attacks through adaptive sliding mode approach

Performance-based event-triggered observer design for security control of cyber-physical systems with malice attacks through adaptive sliding mode approach

Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI.

Brain-computer interfaces use signals from the brain, such as EEG, to determine brain states, which in turn can be used to issue commands, for example, to control industrial machinery. While Cloud computing can aid in the creation and operation of industrial multi-user BCI systems, the vast amount of data generated from EEG signals can lead to slow response time and bandwidth problems. Fog computing reduces latency in high-demand computation networks. Hence, this paper introduces a fog computing solution for BCI processing. The solution consists in using fog nodes that incorporate machine learning algorithms to convert EEG signals into commands to control a cyber-physical system. The machine learning module uses a deep learning encoder to generate feature images from EEG signals that are subsequently classified into commands by a random forest. The classification scheme is compared using various classifiers, being the random forest the one that obtained the best performance. Additionally, a comparison was made between the fog computing approach and using only cloud computing through the use of a fog computing simulator. The results indicate that the fog computing method resulted in less latency compared to the solely cloud computing approach.

Flow-induced oscillations of pitching swept wings: stability boundary, vortex dynamics and force partitioning

We study experimentally the aeroelastic instability boundaries and three-dimensional vortex dynamics of pitching swept wings, with the sweep angle ranging from 0 $^\circ$ to 25 $^\circ$ . The structural dynamics of the wings are simulated using a cyber-physical control system. With a constant flow speed, a prescribed high inertia and a small structural damping, we show that the system undergoes a subcritical Hopf bifurcation to large-amplitude limit-cycle oscillations (LCOs) for all the sweep angles. The onset of LCOs depends largely on the static characteristics of the wing. The saddle-node point is found to change non-monotonically with the sweep angle, which we attribute to the non-monotonic power transfer between the ambient fluid and the elastic mount. An optimal sweep angle is observed to enhance the power extraction performance and thus promote LCOs and destabilize the aeroelastic system. The frequency response of the system reveals a structural-hydrodynamic oscillation mode for wings with relatively high sweep angles. Force, moment and three-dimensional flow structures measured using multi-layer stereoscopic particle image velocimetry are analysed to explain the differences in power extraction for different swept wings. Finally, we employ a physics-based force and moment partitioning method to correlate quantitatively the three-dimensional vortex dynamics with the resultant unsteady aerodynamic moment.

Robust stutter bisimulation for abstraction and controller synthesis with disturbance

This paper proposes a method to synthesise controllers for cyber-physical systems subjected to disturbances, such that the controlled system satisfies specifications given as linear temporal logic formulas. To solve this problem, a finite-state abstraction of the original system is first constructed, and then a controller is synthesised for the abstraction. Due to the disturbances and uncertainty in the environment, future states cannot be predicted exactly, and the abstraction must take this into account. For this purpose, the robust stutter bisimulation relation is introduced, which preserves the existence of controllers for any given linear temporal logic formula that excludes the next operator. States are related by the robust stutter bisimulation relation if the same target sets can be guaranteed to be reached or avoided under control of some controller, thus ensuring that disturbances have similar effect on paths that start in related states. It is shown that there exists a controller enforcing a linear temporal logic formula for the original system if and only if a controller exists for the abstracted system. The approach is illustrated by a robot navigation example.

Research on Secure State Estimation and Recovery Control for CPS under Stealthy Attacks

As the application of cyber-physical systems (CPSs) becomes more and more widespread, its security is becoming a focus of attention. Currently, there has been much research on the security defense of the physical layer of the CPS. However, most of the research only focuses on one of the aspects, for example, attack detection, security state estimation, or recovery control. Obviously, the effectiveness of security defense targeting only one aspect is limited. Therefore, in this paper, a set of security defense processes is proposed for the case that a CPS containing multiple sensors is subject to three kinds of stealthy attacks (i.e., zero-dynamics attack, covert attack, and replay attack). Firstly, the existing attack detection method based on improved residuals is used to detect stealthy attacks. Secondly, based on the detection results, an optimal state estimation method based on improved Kalman filtering is proposed to estimate the actual state of the system. Then, based on the optimal state, internal model control (IMC) is introduced to complete the recovery control of the system. Finally, the proposed methods are integrated to give a complete security defense process, and the simulation is verified for three kinds of stealthy attacks. The simulation results show that the proposed methods are effective.

Towards DevOps for Cyber-Physical Systems (CPSs): Resilient Self-Adaptive Software for Sustainable Human-Centric Smart CPS Facilitated by Digital Twins

The Industrial Revolution drives the digitization of society and industry, entailing Cyber-Physical Systems (CPSs) that form ecosystems where system owners and third parties share responsibilities within and across industry domains. Such ecosystems demand smart CPSs that continuously align their architecture and governance to the concerns of various stakeholders, including developers, operators, and users. In order to satisfy short- and long-term stakeholder concerns in a continuously evolving operational context, this work proposes self-adaptive software models that promote DevOps for smart CPS. Our architectural approach extends to the embedded system layer and utilizes embedded and interconnected Digital Twins to manage change effectively. Experiments conducted on industrial embedded control units demonstrate the approach’s effectiveness in achieving sub-millisecond real-time closed-loop control of CPS assets and the simultaneous high-fidelity twinning (i.e., monitoring) of asset states. In addition, the experiments show practical support for the adaptation and evolution of CPS through the dynamic reconfiguring and updating of real-time control services and communication links without downtime. The evaluation results conclude that, in particular, the embedded Digital Twins can enhance CPS smartness by providing service-oriented access to CPS data, monitoring, adaptation, and control capabilities. Furthermore, the embedded Digital Twins can facilitate the seamless integration of these capabilities into current and future industrial service ecosystems. At the same time, these capabilities contribute to implementing emerging industrial services such as remote asset monitoring, commissioning, and maintenance.

Towards a novel cyber physical control system framework: a deep learning driven use case

PurposeThe health crisis has highlighted the shortcomings of the industry sector which has revealed its vulnerability. To date, there is no guarantee of a return to the “world before”. The ability of companies to cope with these changes is a key competitive advantage requiring the adoption/mastery of industry 4.0 technologies. Therefore, companies must adapt their business processes to fit into similar situations.Design/methodology/approachThe proposed methodology comprises three steps. First, a comparative analysis of the existing CPSs is elaborated. Second, following this analysis, a deep learning driven CPS framework is proposed highlighting its components and tiers. Third, a real industrial case is presented to demonstrate the application of the envisioned framework. Deep learning network-based methods of object detection are used to train the model and evaluation is assessed accordingly.FindingsThe analysis revealed that most of the existing CPS frameworks address manufacturing related subjects. This illustrates the need for a resilient industrial CPS targeting other areas and considering CPSs as loopback systems preserving human–machine interaction, endowed with data tiering approach for easy and fast data access and embedded with deep learning-based computer vision processing methods.Originality/valueThis study provides insights about what needs to be addressed in terms of challenges faced due to unforeseen situations or adapting to new ones. In this paper, the CPS framework was used as a monitoring system in compliance with the precautionary measures (social distancing) and for self-protection with wearing the necessary equipments. Nevertheless, the proposed framework can be used and adapted to any industrial or non-industrial environments by adjusting object detection purpose.