Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection b...
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Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection between cyberspace and physical processes results in the exposure of industrial production information to unprecedented security risks. It is imperative to develop suitable strategies to ensure cyber security while meeting basic performance *** the perspective of controlengineering, this review presents the most up-to-date results for privacy-preserving filtering,control, and optimization in industrial cyber-physical systems. Fashionable privacy-preserving strategies and mainstream evaluation metrics are first presented in a systematic manner for performance evaluation and engineering *** discussion discloses the impact of typical filtering algorithms on filtering performance, specifically for privacy-preserving Kalman filtering. Then, the latest development of industrial control is systematically investigated from consensus control of multi-agent systems, platoon control of autonomous vehicles as well as hierarchical control of power systems. The focus thereafter is on the latest privacy-preserving optimization algorithms in the framework of consensus and their applications in distributed economic dispatch issues and energy management of networked power systems. In the end, several topics for potential future research are highlighted.
The growing availability of affordable Virtual Reality (VR) hardware and the increasing interest in the Metaverse are driving the expansion of Social VR (SVR) platforms. These platforms allow users to embody avatars i...
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The growing availability of affordable Virtual Reality (VR) hardware and the increasing interest in the Metaverse are driving the expansion of Social VR (SVR) platforms. These platforms allow users to embody avatars in immersive social virtual environments, enabling real-time interactions using consumer devices. Beyond merely replicating real-life social dynamics, SVR platforms offer opportunities to surpass real-world constraints by augmenting these interactions. One example of such augmentation is Artificial Facial Mimicry (AFM), which holds significant potential to enhance social experiences. Mimicry, the unconscious imitation of verbal and non-verbal behaviors, has been shown to positively affect human-agent interactions, yet its role in avatar-mediated human-to-human communication remains under-explored. AFM presents various possibilities, such as amplifying emotional expressions, or substituting one emotion for another to better align with the context. Furthermore, AFM can address the limitations of current facial tracking technologies in fully capturing users' emotions. To investigate the potential benefits of AFM in SVR, an automated AM system was developed. This system provides AFM, along with other kinds of head mimicry (nodding and eye contact), and it is compatible with consumer VR devices equipped with facial tracking. This system was deployed within a test-bench immersive SVR application. A between-dyads user study was conducted to assess the potential benefits of AFM for interpersonal communication while maintaining avatar behavioral naturalness, comparing the experiences of pairs of participants communicating with AFM enabled against a baseline condition. Subjective measures revealed that AFM improved interpersonal closeness, aspects of social attraction, interpersonal trust, social presence, and naturalness compared to the baseline condition. These findings demonstrate AFM's positive impact on key aspects of social interaction and highlight its pote
The increasing prevalence of drones has raised significant concerns regarding their potential for misuse in activities such as smuggling, terrorism, and unauthorized access to restricted airspace. Consequently, the de...
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In this work, we present a model-based optimal boundary control design for an aerial robotic system composed of a quadrotor carrying a flexible cable. The whole system is modeled by partial differential equations (PDE...
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The control of robot manipulators presents significant challenges, primarily due to their complex, nonlinear dynamics. Another major difficulty arises from environmental and operational disturbances. Numerous control ...
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Hydrogen is an energy carrier that can support the development of sustainable and flexible energy systems. However, decarbonization can occur when green sources are used for energy production and appropriate water use...
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In the process industries, it's hard to control a non-linear process. Nonlinear behavior is frequently seen in real processes. The challenging problem of controlling a spherical tank is result of its nonlinearity ...
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Since the successful Apollo program, humanity is once again aiming to return to the Moon for scientific discovery, resource mining, and inhabitation. Upcoming decades focus on building a lunar outpost, with robotic sy...
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This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking pe...
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This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.
The AC-DC Energy Nodes (ADENs) concept offers a transformative approach to modernizing power grids, particularly in the context of supergrids. By centralizing power flows from diverse renewable energy sources, such as...
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