Self-adaptive control in cyber-physical systems

Cyber-physical Systems (CPS) combine human-machine interaction, the physical world around us, and software aspects by integrating physical systems with communication networks. Opportunities and research challenges are largely interconnected with the three core sub-domains of CPS m computation, communication and control. The current state of the art of the legacy communication technology is one of the major hindrances limiting the evolution of CPS. Most specifically, innovation in communication is restricted with existing routing and switching technologies leaving no practical methods for researchers to test their new ideas. Software Defined Networking (SDN), through the realization of OpenFlow, separates network control logic from the underlying physical routers and switches. This phenomenon allows researchers to write high-level control programs specifying the behavior of the core networks used to implement CPS and thus, enable innovation in next generation communication architectures for CPS. In this paper, we propose a SDN architecture for industrial automation. Network design requirements are extracted from formal component specifications which support the generation of automatic network configurations. The proposed SDN architecture aims to leverage Industry 4.0 and Smart Factories, to bring together industrial automation installations with networking and Internet technologies.

Cyber-Physical System (CPS) is a symbol of the fourth industrial revolution (4IR) by integrating physical and computational processes which can associate with humans in various ways. In short, the relationship between Cyber networks and the physical component is known as CPS, which is assisting to incorporate the world and influencing our ordinary life significantly. In terms of practical utilization of CPS interacting abundant difficulties. Currently, CPS is involved in modern society very vastly with many uptrend perspectives. All the new technologies by using CPS are accelerating our journey of innovation. In this paper, we have explained the research areas of 14 important domains of Cyber-Physical Systems (CPS) including aircraft transportation systems, battlefield surveillance, chemical production, energy, agriculture (food supply), healthcare, education, industrial automation, manufacturing, mobile devices, robotics, transportation, and vehicular. We also demonstrated the challenges and future direction of each paper of all domains. Almost all articles have limitations on security, data privacy, and safety. Several projects and new dimensions are mentioned where CPS is the key integration. Consequently, the researchers and academicians will be benefited to update the CPS workspace and it will help them with more research on a specific topic of CPS. 158 papers are studied in this survey as well as among these, 98 papers are directly studied with the 14 domains with challenges and future instruction which is the first survey paper as per the knowledge of authors.

Cyber-physical systems (CPSs), which integrate sensing, computation, and control, are widely deployed in safety-critical applications such as industrial monitoring and predictive maintenance. Industrial fault diagnosis systems (IFDSs), as a representative CPS, enable intelligent health monitoring but are increasingly vulnerable to stealthy adversarial attacks—carefully crafted input perturbations that mislead models while remaining imperceptible. These attacks compromise system safety, highlighting the urgent need for learning-based security control in CPS. This article proposes multilayered noisy mixup (MLNM)-frequency shift (FrSh), a theoretically grounded adversarial defense framework for IFDS under real-world conditions involving nonstationary signals and class imbalance. The framework integrates two components: multilayered noisy mixup (MLNM) and frequency shift (FrSh). MLNM injects structured additive and multiplicative noise during training and is theoretically proven to introduce first- and second-order regularization terms in the loss function, enhancing generalization and gradient stability. FrSh performs spectrum-aware transformations that preserve conjugate symmetry and spectral integrity, with formal guarantees of aliasing-free, reversible frequency mapping, facilitating effective learning of high-frequency diagnostic features. Extensive experiments on benchmark datasets demonstrate that MLNM-FrSh significantly improves adversarial robustness and standard accuracy compared to state-of-the-art baselines. This work contributes a reliable and theoretically principled solution for attack-resilient fault diagnosis, advancing the broader goal of intelligent security control in CPS.

Cyber physical systems (CPS) is the integration of three processes: physical process, communication process and computation process. Due to the high demand of safety and real- time feature, CPS requires massive real-time spatial and temporal messages. Therefore, representation and demonstration of spatio- temporal information is vital in every stage, such as specification, design, operation, control, and supervision of CPS. In this paper, we propose a method combining clock theory and RCC-8 together indicating spatio-temporal information of CPS, and we present spatio-temporal sequence diagram for change tracking under the specific scene. We take vehicle system as instance to see how the method work in description of spatio-temporal information between vehicles and vehicles, vehicles and people, vehicle and surroundings in different situations.

In a time when technology changes continuously, where things you need today to run a certain system, might not be needed tomorrow anymore, security is a constant requirement. No matter what systems we have, or how we structure them, no matter what means of digital communication we use, we are always interested in aspects like security, safety, privacy. An example of the ever-advancing technology are cyber-physical systems. We propose a complex security architecture that integrates several consecrated methods such as cryptography, steganography and digital signatures. This architecture is designed to not only ensure security of communication by transforming data into secret code, it is also designed to control access to the system and detect and prevent cyber attacks.

International audience

IntroductionThe increasing complexity of cyber-physical systems (CPS) demands robust and efficient action recognition frameworks capable of seamlessly integrating multi-modal data. Traditional methods often lack adaptability and perform poorly when integrating diverse information sources, such as spatial and temporal cues from diverse image sources.MethodsTo address these limitations, we propose a novel Multi-Scale Attention-Guided Fusion Network (MSAF-Net), which leverages advanced image fusion techniques to significantly enhance action recognition performance in CPS environments. Our approach capitalizes on multi-scale feature extraction and attention mechanisms to dynamically adjust the contributions from multiple modalities, ensuring optimal preservation of both structural and textural information. Unlike conventional spatial or transform-domain fusion methods, MSAF-Net integrates adaptive weighting schemes and perceptual consistency measures, effectively mitigating challenges such as over-smoothing, noise sensitivity, and poor generalization to unseen scenarios.ResultThe model is designed to handle the dynamic and evolving nature of CPS data, making it particularly suitable for applications such as surveillance, autonomous systems, and human-computer interaction. Extensive experimental evaluations demonstrate that our approach not only outperforms state-of-the-art benchmarks in terms of accuracy and robustness but also exhibits superior scalability across diverse CPS contexts.DiscussionThis work marks a significant advancement in multi-modal action recognition, paving the way for more intelligent, adaptable, and resilient CPS frameworks. MSAF-Net has strong potential for application in medical imaging, particularly in multi-modal diagnostic tasks such as combining MRI, CT, or PET scans to enhance lesion detection and image clarity, which is essential in clinical decision-making.

Recent developments in computer and communication technologies are leading to an increasingly networked and wireless world. This raises new challenging questions in the context of control for cyber-physical systems (CPS), especially when the computation, communication, energy and actuation resources (for control) of the system are limited and/or shared by multiple control tasks. These limitations obstruct the use of classical design techniques for feedback control algorithms and call for new resource-aware control paradigms. These new resource-aware control systems typically have to take both discrete decisions (which task is allowed to use the resource) and continuous decisions (which continuous control input is generated for the task). In this talk two approaches are presented to address this hybrid co-design problem. Both approaches result in control algorithms that exploit real-time measurement information available on the state of the CPS and decide dynamically on the actions to take. This leads to the situation that individual control tasks are no longer executed in classical periodic time-triggered patterns, but in aperiodic patterns with varying inter-execution times. By abandoning the periodic scheduling of control tasks, the aim is to realise better trade-offs between the overall performance of the CPS and the required resource utilisation. The approaches are illustrated by various applications. interesting challenges for the future are discussed as well.

Event-triggered secure observer-based control for cyber-physical systems under adversarial attacks

This article concerns the problem of observer-based event-triggered control for cyber-physical systems (CPSs) under denial-of-service (DoS) attacks. In contrast to the existing studies where DoS attacks on different channels are the same, the considered attacks compromise each channel independently. Correspondingly, a decentralized event-triggered scheme is adopted based on the tradeoff between the transmission efficiency and tolerable attack intensity with guarantees on the closed-loop stability. Inspired by the Lyapunov theory for switched systems, the proposed stabilization criteria reveals a link between the tolerable attack intensity and the event-triggering parameters. An example is finally provided to illustrate the effectiveness of the proposed approaches.

Distributed finite-time fault estimation and fault-tolerant control for cyber-physical systems with matched uncertainties

There is a growing interest in design and implementation of cyber-physical control systems over wireless sensor and actuator networks (WSANs). Thanks to the use of wireless communications and distributed architectures, these systems encompass many advantages as compared to traditional networked control systems using hard wirelines. While WSANs are enabling a new generation of control systems, they also introduce considerable challenges to quality-of-service (QoS) provisioning. In this chapter, we examine some of the major QoS challenges raised by WSANs, including resource constraints, platform heterogeneity, dynamic network topology, and mixed traffic. These challenges make it difficult to fulfill the requirements of cyber-physical control in terms of reliability and real time. The focus of this chapter is on addressing the problem of network reliability. Specifically, we analyze the behavior of wireless channels via simulations based on a realistic link-layer model. Packet loss rate (PLR) is taken as a major metric for the analysis. The results confirm the unreliability of wireless communications and the uncertainty of packet loss over WSANs. To tackle packet loss, we present a simple solution that can take advantage of existing prediction algorithms. Simulations are conducted to evaluate the performance of several classical prediction algorithms used for loss compensation. The results give some insights into how to deal with packet loss in cyber-physical control systems over unreliable WSANs.

Cyber-Physical Systems (CPS) integrate discrete-time computing and continuous-time physical-world entities, which are often wirelessly interlinked. The use of wireless safety critical CPS (control, healthcare etc.) requires safety guarantees despite communication faults. This paper focuses on one important set of such safety rules: Proper-Temporal-Embedding (PTE). Our solution introduces hybrid automata to formally describe and analyze CPS design patterns. We propose a novel lease based design pattern, along with closed-form configuration constraints, to guarantee PTE safety rules under arbitrary wireless communication faults. We propose a formal methodology to transform the design pattern hybrid automata into specific wireless CPS designs. This methodology can effectively isolate physical world parameters from affecting the PTE safety of the resultant specific designs. We conduct a case study on laser tracheotomy wireless CPS to show that the resulting system is safe and can withstand communication disruptions.

Cyber-physical systems (CPS) are considered the integration of physical systems in the real world and control software in computing systems. In CPS, the real world and the computing systems are connected through networks with real-time information exchange. The introduction of networking technologies in CPS has substantial advantages in terms of system efficiency, scalability, maintenance, and many more. However, CPS are vulnerable to malicious attackers intruding on the network. The attackers aim to destroy the physical systems with cyber-physical attacks, in which the main objective is to remotely cause malfunctions in physical systems through networks. Therefore, extensive research on cyber-physical security has been conducted to detect cyber-physical attacks and guarantee the stability of the physical systems under cyber-physical attacks. In this survey, we conduct an exhaustive review of the literature on threats to CPS and on resilient CPS design strategies. First, we discuss the structure of CPS, considering physical dynamics, computing systems, and networks. Then, we provide a taxonomy of cyber-physical attacks with three properties (attack space, attack location, and stealthiness). These three attributes represent the requirements for the implementation of cyber-physical attacks. We review existing studies on anomaly-detection strategies against cyber-physical attacks in terms of physics, networks, and machine learning techniques. Moreover, we analyze the impact of typical cyber-physical attacks and the constraints on attack implementations. In particular, for each of the well-established cyber-physical attacks, we present numerical examples that clearly illustrate the time responses of the physical systems with a conventional physics-based anomaly detector. We also review advances in the typical cyber-physical attacks and the detection methods for these attacks. Furthermore, we review resilient CPS designs in the context of control theory, network management, and data-driven technologies. We conclude the survey by identifying future research directions.

Cyber-physical systems are increasingly developed for highly-automated purposes, such as industrial production plants. Not only the level of autonomy of these cyber-physical production systems, but also their potential lifespan, and the expectation that the system adapts throughout its life introduces challenges for the development process. To develop such systems in a structured manner, consideration of their context is essential. Suitable abstractions and perspectives must be employed to highlight different aspects of the context, such as structural or functional concerns. To ensure longevity and adaptability, the development process must account for changes in the context at runtime. This introduces a great deal of uncertainty. Current uncertainty modeling approaches cannot be applied right away, as they do not explicitly consider the multiple aspects of context analysis, which need to be documented in different artifacts. To aid the engineering of collaborative cyber-physical production systems, this paper presents a model-based approach to document context uncertainty. We propose “Orthogonal Uncertainty Models”, which closely integrate with context models from different perspectives. The approach has been evaluated using a case example from the industry automation domain, which is also presented herein.