What are Cyber-Physical Systems (CPS)? The Future of AI and Automation

Cyber-Physical Systems (CPS) represent the advanced integration of computational processes, networking, and physical environments. Unlike conventional embedded systems, a CPS utilizes sensors and actuators to link the physical world with real-time virtual algorithms, establishing itself as the backbone of Industry 4.0.

In the midst of the digital transformation era, CPS has emerged as the cornerstone of innovation. In simple terms, a cyber-physical system is a mechanism overseen by computer algorithms and deeply integrated with the Internet. Within this ecosystem, software and physical components converge to operate across various temporal and spatial scales, interacting to transform our tangible reality.

This technology constitutes a transformative fusion that is shaping up to be one of the pillars of the fourth and fifth industrial revolutions. In fact, CPS are redefining the engineering paradigm with profound applications in strategic sectors such as energy, agriculture, healthcare, transportation, and manufacturing (Tushar et al., 2023).

In this article, we will analyze the principles of cyber-physical systems, their challenges, and the decisive role they play in modern industry. Furthermore, we will explore their typologies, the critical importance of security, and the scientific trends defining the future of this technological discipline.

Key Points

• Definitive Convergence of the Digital and Physical Realms: Unlike traditional systems, CPS do not merely process data; they operate within a continuous feedback loop with the tangible world. They employ sensors and actuators to translate virtual information into precise, real-time physical actions.

• The Heart of Industry 4.0: CPS are the architects of Smart Factories. Leveraging the 5C Model architecture (Connection, Conversion, Cyber, Cognition, and Configuration), they enable machinery autonomy, reducing operational costs by up to 25% through predictive maintenance.

• Critical Distinction from IoT: It is vital to understand that while IoT focuses on connectivity and data collection, CPS centers on deep interaction and control. If IoT observes the world, CPS possesses the inherent capacity to modify and manage it.

• Cybersecurity as Physical Safety: Within the CPS ecosystem, a digital vulnerability can lead to catastrophic physical consequences, such as the sabotage of electrical grids or medical equipment. Consequently, current trends are shifting toward Zero Trust Architecture (ZTA) and the use of Digital Twins for secure attack simulations.

• The Future in CPAI (Cyber-Physical AI): The next major technological leap is Cyber-Physical Artificial Intelligence. This field seeks to harmonize the probabilistic nature of AI with the absolute certainty required by physical systems, ensuring that future autonomous systems are both intelligent and infallible.

What are Cyber-Physical Systems? Technical Definition and Evolution

At their core, Cyber-Physical Systems (CPS) constitute the convergence of computational algorithms with tangible physical processes. According to Yao et al. (2019), the primary objective of these systems is to create a bidirectional communication interface between the digital and physical worlds. Furthermore, Javaid et al. (2023) emphasize that CPS represent a new generation of integrations where computing and networks are intrinsically fused with physical assets.

Regarded as the ultimate evolution of mechatronic engineering and computer science, CPS are defined as technological orchestrations that link ‘cyberspace’ (software and algorithms) with ‘physical space’ (machinery, environment, and the human factor).

In simple terms, they are integrated networks that monitor and control physical processes through a constant feedback loop. This connection enables real-time interactions, resulting in autonomous and highly efficient systems. As noted by El-Kady et al. (2023), a CPS can be monitored, controlled, and operated remotely thanks to an accurate, real-time perception of reality.

Currently, these systems play a vital role in strategic sectors such as healthcare, transportation, energy, and manufacturing. Through the use of sensors, actuators, and data networks, CPS possess the capacity for autonomous decision-making, driving the development of a smarter and more interconnected world.

History and Evolution of Cyber-Physical Systems (CPS)

The concept of cyber-physical systems is not recent; its roots trace back to the early 2000s when the National Science Foundation (NSF) identified the potential of integrating computing with physics. The term was officially coined in 2006 by Helen Gill during an NSF-organized workshop (Alguliyev et al., 2018), a milestone that catalyzed global research and funding programs.

Since their inception, CPS have evolved exponentially due to the increase in computing power, Artificial Intelligence (AI), and advanced connectivity. The massive adoption of the Internet of Things (IoT) and the deployment of 5G networks have accelerated this transformation. In this context, Chui et al. (2023) predict that as IoT consolidates as the dominant network architecture, its role will become increasingly critical in the refinement and expansion of cyber-physical systems.

Key Differences: Cyber-Physical Systems (CPS) vs. the Internet of Things (IoT)

Oftentimes, the terms CPS and IoT are used interchangeably; however, there exists a subtle yet momentous distinction. While the Internet of Things (IoT) primarily focuses on connectivity and data exchange between devices, Cyber-Physical Systems emphasize closed-loop control and deep, bidirectional interaction between computation and physical processes.

As reported by Gurung et al. (2026), IoT and CPS are frequently analyzed together because they share similar architectures, operational characteristics, and hardware components, in addition to facing virtually identical security challenges.

Despite these similarities, it is fundamental to understand that their operational objectives differ notably:

Feature	Internet of Things (IoT)	Cyber-Physical Systems (CPS)
Primary Focus	Connectivity and massive data collection.	Advanced control and physical feedback.
Intelligence	Generally centralized in the Cloud.	Distributed and real-time (Edge Computing).
Interaction	Predominantly passive (monitoring).	Active (modifies the state of the physical world).
Typical Example	A Wi-Fi-enabled smart thermostat.	A self-regulating power plant.

Fundamental Principles of Cyber-Physical Systems

The design and operation of CPS are built upon strategic pillars that ensure autonomous, secure, and efficient functionality. These principles allow systems to adapt dynamically to their environment, offering unprecedented versatility across various industries:

• Intrinsic Integration: CPS executes a deep fusion between computation and physical processes. This implies that hardware and software do not merely coexist but operate in tandem as an indivisible functional unit.
• Feedback Loops: These systems rely on constant feedback. They utilize advanced sensors for data acquisition and precision actuators to execute decisions that directly impact the physical world.
• Real-Time Operability: The capacity for processing and responding to environmental changes must occur in real time. This principle is critical to ensuring operational precision and, above all, safety in industrial or mission-critical environments.
• Autonomy and Decision-Making: Through the use of advanced algorithms and Machine Learning, CPS can manage complex tasks without human intervention, optimizing processes independently.

Thanks to the convergence of these principles, CPS achieves superior responsiveness, establishing itself as a robust solution for the challenges of modern engineering.

Classification and Typologies of Cyber-Physical Systems

The taxonomy of CPS varies according to their level of complexity, functionality, and the operational environment in which they are deployed. Below are the primary categories defining the current ecosystem of this technology:

• Networked Control Systems (NCS): These architectures utilize advanced communication technologies for the remote supervision and control of physical assets, ensuring long-distance operability.
• Integrated Cyber-Physical Systems: These represent small-scale solutions found in everyday devices, such as smartphones, wearables, and smart home ecosystems.
• Critical Infrastructure Systems: These are CPS designed for the management of essential services, including smart grids, water supply systems, and transportation logistics networks.
• Industrial Automation Systems: They constitute the core of modern manufacturing plants, enabling real-time monitoring, process optimization, and predictive maintenance.
• Medical Cyber-Physical Systems (MCPS): Applied to the healthcare sector, these systems range from constant vital sign monitoring to high-precision surgical robotics.

Although each typology faces specific technical and security challenges, they all converge toward a common purpose: providing physical systems with superior intelligence and a dynamic response capacity to external stimuli.

Cyber-Physical Systems Architecture: The 5C Model

For a system to achieve an advanced CPS hierarchy, it must be grounded in a structured architecture. The most recognized standard in the Industry 4.0 ecosystem is the 5C Model, which defines five critical layers of implementation:

• Connection: Implementation of sensors and industrial communication protocols (such as MQTT and OPC UA) designed for precise data acquisition directly from the physical source.
• Conversion: A processing phase where raw data is transformed into strategic information through asset health algorithms and advanced data analytics.
• Cyber: Development of a Digital Twin, a virtual replica that functions as the central core of intelligence and simulation for the physical system.
• Cognition: A diagnostic stage where processed information is presented to facilitate decision-making, identifying wear patterns, anomalies, or operational inefficiencies.
• Configuration: The self-management level where the system issues corrective commands back to the physical world, autonomously adjusting parameters to eliminate deviations.

💡 Engineering Strategic Value: The rigorous implementation of the 5C Model can reduce operational costs by up to 25%. This is achievable through predictive maintenance, which allows for intervention on assets before a critical breakdown occurs on the production line.

Cyber-Physical Systems and the Fourth Industrial Revolution

The Fourth Industrial Revolution, or Industry 4.0, defines the current trend toward intelligent automation and massive data exchange within manufacturing ecosystems. In this scenario, CPS act as the primary driver of change by facilitating an unprecedented fusion between digital processes and the physical environment.

This integration gives rise to so-called Smart Factories—environments where machinery possesses the capability to communicate autonomously, make critical decisions, and optimize production flows without the need for constant human intervention. This advancement not only drastically increases productivity but also minimizes resource waste, promoting a far more sustainable and efficient manufacturing model.

Beyond the factory floor, CPS is fundamental in optimizing supply chain management, advanced logistics, and predictive maintenance. As concluded by Aslam et al. (2026), the synergy between cyber-physical systems and Industry 4.0 is transforming strategic sectors such as Intelligent Transportation Systems (ITS) by hybridizing high-level computational capabilities with resilient physical infrastructures.

Applications of Cyber-Physical Systems: Transforming Global Industry

The applications of CPS are vast and constitute the pillar of modern industry (Duo et al., 2022). As detailed by Alguliyev et al. (2018), these systems serve as the foundation for the development of smart infrastructure, connected cities, autonomous vehicles, and advanced defense and meteorology systems.

Below, we analyze the impact of CPS across strategic sectors:

Water Management

Facing the challenges of climate change, CPS plays a crucial role in water governance. Alexandra et al. (2023) highlight their functionality in rural, urban, and coastal environments to optimize the entire water cycle.

Healthcare and Smart Medicine

Medical Cyber-Physical Systems (MCPS), such as advanced pacemakers and surgical robots, have revolutionized clinical care. Research by Liu et al. (2023) highlights the use of Explainable AI (XAI) to enhance transparency and reliability in medical imaging analysis, facilitating precise remote diagnostics.

Transportation and Mobility

The autonomous vehicle is the foremost exponent in this sector. By processing real-time data from sensors and GPS, these systems autonomously optimize routes and prevent collisions.

Energy and Smart Grids

Smart grids manage electrical distribution efficiently. However, Dui et al. (2026) warn that integrating renewable energy into microgrids requires a joint risk assessment, as vulnerability to cyberattacks can compromise energy flow.

Advanced Manufacturing (ICPS)

In Smart Factories, Industrial Cyber-Physical Systems enable full cooperation across the value chain (Zhang et al., 2022). Authors such as Ryalat et al. (2023) propose innovative frameworks that integrate information and communication technologies to maximize industrial operability.

Precision Agriculture

Precision agriculture techniques utilize CPS to monitor soil conditions, weather patterns, and crop health, allowing farmers to optimize resource use and increase yields.

Food and Preventive Health

CPS offers scalable solutions for real-time nutritional monitoring. Biskupovic et al. (2026) demonstrated that identifying dietary patterns facilitates the integration of data-driven preventive medical services.

Construction and Automation

The future of construction lies in automation and real-time control. Akanmu et al. (2021) point out that Digital Twins and next-generation CPS are essential for accelerating innovation in physical facilities.

Challenges and Limitations of Cyber-Physical Systems

While the potential of CPS is disruptive, its implementation entails a series of challenges and critical drawbacks that organizations must rigorously evaluate:

• High Investment Costs: Deploying these systems requires substantial capitalization in specialized hardware, cutting-edge software, and robust network infrastructure. For small and medium-sized enterprises (SMEs), the initial investment can represent a significant barrier to entry.
• Technical and Operational Complexity: The design, integration, and maintenance of CPS demand a high level of specialization. The tight convergence between physical and digital components requires extremely sophisticated technical management to avoid synchronization errors.
• Cybersecurity Vulnerabilities: Due to their interconnected nature, CPS are prime targets for cyberattacks. A security breach not only compromises data integrity but can also lead to tangible physical damage or critical disruptions in essential infrastructure.
• Critical Connectivity Dependency: The operability of most CPS is contingent upon a stable, low-latency internet connection. Any network interruption can trigger operational inefficiencies or systemic failures in real-time processes.**

Despite these factors, long-term strategic benefits typically outweigh initial drawbacks, provided that a resilient infrastructure and next-generation security protocols are guaranteed.

Strategic Challenges and Cybersecurity in CPS

To unlock the full potential of Cyber-Physical Systems (CPS), it is imperative to address technical challenges that extend beyond traditional computing. The integration of legacy infrastructure, the need for massive real-time processing, and the urgency for global regulatory frameworks are barriers that the industry must dismantle to ensure secure, large-scale adoption.

Security as a Critical Pillar

One of the most acute issues is vulnerability to cyberattacks. Since these systems manage essential services, a security breach could collapse electrical grids or sabotage vital medical devices. As noted by Duo et al. (2022), protecting the physical, cyber, and communication layers is currently an absolute priority for researchers and professionals.

The digital-physical convergence introduces unprecedented risks: a software vulnerability no longer merely implies information loss; it can translate into the physical destruction of a turbine or the sabotage of a water treatment plant.

Industrial Cybersecurity Vulnerabilities

Protecting a CPS presents unique challenges that do not exist in conventional office environments:

• Data Injection: Attacks where sensors are deceived into reporting normalcy while the system operates under critical conditions.
• Critical Latency: In a CPS, security cannot slow down the process. A delay of just 10 ms in an automatic braking system is unacceptable.
• Legacy Systems: Much of the critical infrastructure uses old protocols designed before the era of hyper-connectivity.**

💡 Security Notice: Currently, Zero Trust Architecture (ZTA) is emerging as the standard recommended by the NSF and NIST to secure complex CPS ecosystems.

Threat Taxonomy in Digital Twins and CPS

According to Otoom (2025) and Canonico & Sperlì (2023), threats are categorized based on their impact on system availability, integrity, and control:

Attack Category	Description and Consequence
Denial of Service (DoS/DDoS)	System overload that interrupts data flow and causes operational shutdowns.
Data Tampering	Alteration of sensor measurements (Spoofing) that induces the system into erroneous decisions.
PitM Interception (Person-In-The-Middle)	Modification of the communication flow between the digital twin and the physical asset.
Command Injection	Unauthorized takeover of devices through unauthenticated protocols.
Lateral Movement & Reconnaissance	Silent infiltration to gather intelligence and access sensitive data over the long term.
Physical and Insider Risks	Intentional sabotage or negligence by personnel with authorized access.
Hardware and Firmware Threats	Insertion of Rogue Devices or firmware alteration to create backdoors.
AI Bias Exploitation	Attacks designed to deceive the system’s own security detection models.

Trends in Resilience and Advanced Defense

Current research proposes dynamic solutions to face this hostile landscape. Babar et al. (2026) highlight the use of Hierarchical Reinforcement Learning (HRL) to automatically adapt incident response thresholds. On the other hand, Somers et al. (2023) suggest using Digital Twins as secure testing environments to simulate attacks without risking actual operations.

Finally, Cassottana et al. (2023) emphasize the importance of standardized frameworks to assess CPS resilience, allowing organizations to recover effectively after any disruption.

Scientific Trends in the Field of Cyber-Physical Systems

Geography and National Thematic Focuses

Following the metadata processing of over 7,000 academic publication records from 2021 to 2026, geographic clusters with distinct specializations have been identified:

• China: Leadership in Infrastructure and Network Control
  • Thematic Focus: Absolute dominance in Smart Grids and microgrid control.
  • Specialization: A significant portion of Chinese output centers on False Data Injection Attack (FDIA) detection and distributed control algorithms for power systems. Research trends show a strong presence of the State Grid Corporation and Chinese polytechnic universities leading the way in industrial CPS resilience.
• United States: Security Architecture and Standardization
  • Thematic Focus: Security by Design and Medical CPS.
  • Specialization: Research leans toward system interoperability, autonomous vehicle safety, and the development of theoretical frameworks for the Internet of Medical Things (IoMT). There is a robust link between academia and federal agencies (such as NIST or NSF).
• Europe (Germany, Italy, France): Industry 4.0 and Digital Twins
  • Thematic Focus: Advanced automation and smart manufacturing.
  • Specialization: The European cluster leads the implementation of Digital Twins applied to production lines. There is a notable emphasis on AI ethics and the transition toward Industry 5.0 (human-robot collaboration and sustainability).
• India and Southeast Asia: IoT Applications and Sensors
  • Thematic Focus: Resource optimization and Wireless Sensor Networks (WSN).
  • Specialization: A strong trend toward low-cost solutions in smart agriculture and environmental monitoring via CPS, with a growing focus on using Blockchain to secure data integrity in IoT devices.

Leading Institutions and Research Areas

The analysis of affiliation metadata within the Database reveals an ecosystem where research is distributed among major global technological clusters, with a clear distinction between academic (theoretical) and strategic (applied) science.

Academic Cluster (High-Impact Universities)

• Nanyang Technological University (NTU) & National University of Singapore (NUS):
  • Research Line: Security at the control level and resilience of urban CPS. They focus on intrusion detection algorithms for intelligent transportation systems and water networks.
• Tsinghua University & Zhejiang University (China):
  • Research Line: Advanced industrial process control and smart grids. They lead the development of secure communication protocols for the national power grid and Kalman filtering for state estimation under attack.
• IIT (Indian Institutes of Technology):
  • Research Line: Low-power architectures for IoT and CPS in agriculture. Their focus is on optimizing sensor nodes in environments with limited connectivity.

Strategic and Regulatory Cluster (Institutes and Academies)

• Chinese Academy of Sciences (CAS):
  • Role: Frontier research in AI applied to CPS. A massive volume of publications is observed regarding Deep Learning for failure pattern recognition in complex cyber-physical systems.

Synthesis of Research Nature

• Academic Science: 75% of records originate from universities, focusing on the development of mathematical models and algorithmic optimization.
• Strategic Science: The remaining 25% (national institutes and laboratories) focuses on the resilience of critical infrastructure (energy and defense), suggesting that CPS are viewed as national security assets in leading countries.

Influential Authors and Expert Profiles

Following the analysis of publishing scientists, three clearly differentiated schools of thought have been identified:

The School of Robust Control and Resilience (Central Core)

• Shi, Yang (Green Cluster): Positioned as the author with the highest centrality. His research focuses on Model Predictive Control (MPC) and state estimation for CPS. His work is fundamental to ensuring the operational stability of systems under external attacks or technical failures.
• Chen, Bo (Central Bridge): Acts as a knowledge integrator. His primary focus is on signal processing and information fusion within sensor networks. He is a key figure in anomaly detection and the security of Cyber-Physical Industrial Control Systems (CPICS).
• Garg, Sahil (Central Bridge): Specialist in the application of Artificial Intelligence and Edge Computing. His work bridges control theory with edge processing capabilities, facilitating real-time responses to intrusions.

The School of Software Engineering and Verification (Upper Branch)

• Arrieta, Aitor (Purple Cluster): Leads a sub-community specialized in the Verification and Validation (V&V) of CPS. His approach is preventive, utilizing automated testing techniques to uncover erroneous behaviors during the design phase prior to implementation.
• Challenger, Moharram (Gray Cluster): Expert in Model-Driven Engineering (MDE) and intelligent agents. His work focuses on the automation and sustainability of CPS, using software agents to manage the complexity of smart production lines.

The School of Critical Security and Networks (Center-Right)

• Lee, Insup & Kong, Fanxin (Light Blue Cluster): Represent the core of real-time security research. They focus on sensor data integrity and defense against timing and data injection attacks that can destabilize physical systems, such as autonomous vehicles or medical devices.

Collaboration Network Dynamics

The VOSviewer map reveals a ‘Hub-and-Spoke’ network structure with the following characteristics:

• International Openness vs. Local Cohesion: The Central Core (Green/Red) exhibits high international openness, featuring frequent collaborations between institutions in Asia (China/Singapore) and North America. This indicates that the foundations of CPS control and security are a standardized global effort.
• Peripheral Clusters (Purple/Gray) present more closed or regional collaboration (primarily European), suggesting highly specific technological specialization niches or European industrial consortium projects (such as those under the Horizon 2020 program).
• ‘Bridge’ Actors and Knowledge Flow: Researchers such as Chen, Bo and Garg, Sahil are vital to the network; without them, the central mass would fragment. Their role is to translate advancements in AI and data fusion into practical industrial control applications.
• Temporality and Evolution: Based on node density, a transition is observed: historical authors (such as Shi, Yang) have laid the groundwork for control, while emerging authors (such as Challenger) are driving the field toward Industry 5.0, centered on sustainability and the digital twin.

Thematic Knowledge Map

The metadata analysis reveals that research in Cyber-Physical Systems (CPS) has shifted from being purely theoretical to becoming an ecosystem of ‘digital survival’ and industrial optimization.

Red Cluster: The Resilience and Cyber-Defense Front

This is the densest pillar within the RIS file. Research here is concentrated on data integrity.

• Focus on FDIA (False Data Injection Attacks): There is a critical mass of articles centered on Smart Grids. The challenge is not merely preventing system downtime, but ensuring that an attacker does not ‘deceive’ the controller by injecting false data that causes physical damage.
• SCADA and Industrial Control Systems: A transition toward Machine Learning-based intrusion detection is observed, moving away from traditional rule-based methods.

Green and Orange Clusters: The Smart Factory and the Digital Twin

This block represents the physical application of CPS in Advanced Manufacturing.

• Digital Twins: The RIS identifies this term as the bridge between design and operation. They are utilized not only for simulation but for real-time predictive maintenance.
• Multi-Agent Systems: Research shows a strong trend toward decentralization. There is no longer a single ‘brain’ controller; instead, multiple agents (robots, sensors) make local decisions to increase production flexibility.

Blue and Purple Clusters: Emerging Frontiers (Human and Data)

This is where the field expands into new dimensions:

• Industry 5.0 (The Human Factor): Unlike Industry 4.0, the master file highlights terms such as ‘Human-in-the-loop.’ It investigates how workers interact with the CPS without compromising physical safety or cybersecurity.
• The Impact of COVID-19: RIS records confirm that the pandemic accelerated research into teleoperation and remote laboratories. The purple cluster links the Cloud not just as storage, but as the enabler that allowed industry to continue during lockdowns.

Light Blue Cluster: System Intelligence

• Reinforcement Learning: Identified as the leading technique for Energy Management. CPS are learning to self-optimize to reduce carbon footprints, linking back to the sustainability concepts present in the green cluster.

Emerging Trends and the Future of the Field

Temporal analysis confirms a shift from infrastructure (how to connect systems) toward resilient intelligence (how to protect and optimize autonomous systems).

From ‘Commodity’ to Specialization (2021 vs. 2024+)

• Mature Topics (Blue): Concepts such as Blockchain and Cloud Computing have become the ‘baseline’ or commodities. They are no longer researched in isolation but as integrated tools within broader solutions. Interest in COVID-19 has vanished from recent titles, giving way to pure industrial resilience.
• The Qualitative Leap in Security: While in 2021 the focus was on generic ‘Cybersecurity,’ current records (yellow) focus specifically on False Data Injection Attacks (FDIA). This indicates that the threat is no longer just system downtime, but the system acting destructively based on manipulated data.

Research Fronts: The Future of the Field

After analyzing the abstracts of the most recent records in the RIS file, we identify the three pillars of the immediate future:

• Reinforcement Learning (RL) for Self-Healing: The ‘yellow’ trend shows that AI no longer merely detects attacks; it learns to reconfigure the system in real time to maintain operational stability. This represents a shift from ‘detection’ to ‘defensive autonomy.’
• Industry 5.0 and ‘Human-in-the-loop’: Unlike Industry 4.0, which sought total automation, the master file now emphasizes human factors. The emerging trend investigates the safety of workers collaborating with Cobots (collaborative robots) and how CPS can adapt to human behavior, prioritizing ethics and sustainability.
• Energy Management and Sustainability: CPS are being redesigned as ‘Green Cyber-Physical Systems.’ Control is no longer solely about production efficiency but about minimizing the carbon footprint through energy optimization algorithms in Smart Grids.

Conclusions

• Maturity Status: The field of CPS is currently in its phase of highest practical impact. With over 7,000 records, academic science has evolved into a strategic science applied to national and industrial security.
• Geographic-Institutional Dominance: A shared leadership exists where China dominates control infrastructure (Smart Grids), while Europe and the U.S. lead the transition toward Industry 5.0 and software verification.
• Critical Risk: The sophistication of attacks (FDIA) represents the greatest detected threat. Any future investment in CPS must integrate proactive AI defense (Reinforcement Learning) to remain competitive.

The Future of Cyber-Physical Systems: Toward Cyber-Physical Artificial Intelligence (CPAI)

The horizon for Cyber-Physical Systems (CPS) is as promising as it is complex. As we move into the maturity of the Fourth Industrial Revolution, these systems will continue to lead innovation across critical sectors such as healthcare, transportation, and energy. The convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and Edge Computing will allow CPS to reach unprecedented levels of autonomy and technical sophistication.

However, the success of this evolution depends on overcoming a fundamental dichotomy. Lee et al. (2025) propose the creation of a new interdisciplinary domain: Cyber-Physical Artificial Intelligence (CPAI). This necessity arises from an inherent contradiction within their architectures:

• Traditional AI operates under statistical principles; it relies on the probability that ‘positive events will occur’ and assumes flexible resource availability.
• CPS, conversely, demands absolute certainty under the premise that ‘catastrophic events must never happen,’ operating with strictly limited and shared physical resources.

In short, cyber-physical systems represent the definitive fusion of the digital and tangible worlds. They offer unparalleled opportunities for operational efficiency, advanced automation, and disruptive innovation. As research resolves ethical and security challenges—and succeeds in harmonizing AI probability with CPS precision—this technology will solidify its role as the fundamental pillar shaping the future of global industry.

Frequently Asked Questions about Cyber-Physical Systems (CPS)

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