Operating large-scale scientific facilities requires coordinating diverse subsystems, translating operator intent into precise hardware actions, and maintaining strict safety oversight. Language model-driven agents offer a natural interface for these tasks, but most existing approaches are not yet reliable or safe enough for production use. In this paper, we introduce Osprey, a framework for using agentic AI in large, safety-critical facility operations. Osprey is built around the needs of control rooms and addresses these challenges in four ways. First, it uses a plan-first orchestrator that generates complete execution plans, including all dependencies, for human review before any hardware is touched. Second, a coordination layer manages complex data flows, keeps data types consistent, and automatically downsamples large datasets when needed. Third, a classifier dynamically selects only the tools required for a given task, keeping prompts compact as facilities add capabilities. Fourth, connector abstractions and deployment patterns work across different control systems and are ready for day-to-day use. We demonstrate the framework through two case studies: a control-assistant tutorial showing semantic channel mapping and historical data integration and a production deployment at the Advanced Light Source, where Osprey manages real-time operations across hundreds of thousands of control channels. These results establish Osprey as a production-ready framework for deploying agentic AI in complex, safety-critical environments.

Endothelial ion signaling is crucial for the proper function of the arterial microcirculation, regulating local blood flow to meet metabolic demands and contributing to the regulation of systemic arterial pressure. The role of endothelial ion channels in the precise control of vascular resistance has been primarily investigated in animal models, where the microvasculature is more readily accessible. This review aims to discuss current knowledge on the role of endothelial ion signaling in vasomotor regulation in the human microcirculation, focusing on potassium (K+) channels (KIR2.1, KATP, SKCa/IKCa), Transient Receptor Potential (TRP) channels, particularly TRP Vanilloid 1 (TRPV1) and TRPV4, and Piezo1 channels. The analysis examines the organization of the endothelial ionic signaling machinery in the most extensively studied human microvascular beds, such as the skin, skeletal muscle, and brain, while also discussing vascular reactivity in vessels isolated ex vivo. Accumulating evidence indicates that a distinct repertoire of endothelial ion channels engages diverse endothelium-dependent vasorelaxant pathways across different vascular beds. Understanding how endothelial channels regulate the microvascular unit is predicted to foster the search for alternative therapeutic strategies for treating cardiovascular and neurodegenerative disorders associated with endothelial dysfunction.

The relevance of this study is related to the growing prevalence of drug addiction among the population, which requires a shift from a normative-descriptive to a systemic-cybernetic approach in managing state anti-drug policy. Considering the Strategy as a governing socio-economic system allows for greater efficiency and adaptability of anti-drug governance. The aim of the study was to substantiate the position of the Russian Federation’s state anti-drug Strategy as a cybernetic governing socio-economic system in order to identify its structural elements, control channels, and feedback loops. The methodological framework: principles of systems analysis and cybernetics (N. Wiener, W. Ashby, S. Beer, L. von Bertalanffy, J. Garaedaghi) and employing comparative-analytical, structural-functional, and modeling research methods. Results. The anti-drug Strategy has been shown to possess all the characteristics of a governing system: a goal-oriented approach, a recursive structure, and the presence of feedback channels and information processors. The key role of the meso-level (regional anti-drug commissions) in ensuring the adaptability and sustainability of the entire system has been identified. Conclusion. The Anti-Drug Strategy is formalized as an adaptive, fractal-recursive, and self-organizing cybernetic control system, operating according to the principles of Ashby’s requisite diversity and Beer’s viable system. The application of a systemscybernetic approach improves the manageability, measurability, and sustainability of the implementation of the Russian Federation’s anti-drug policy.

Highly efficient separation and recovery of alkali metal ions (e.g. lithium, sodium, potassium, rubidium, and cesium) from natural and artificial brines are urgent tasks because of the huge reserves of brines and their increasing demand in advanced industries. During the past twenty years, porous metal-organic frameworks (MOFs), with controllable channel structures and chemical micro-environments, have been exploited as new choices in the field of alkali metal ion adsorption and separation. This review focuses on the progress in MOFs and MOF-based adsorbents for the selective capture and co-adsorption of alkali metal ions. The adsorption performances of various MOFs were rationally assessed and compared based on their adsorption capacity, kinetics, selectivity, and regeneration. The mechanisms, including chelation, ion exchange, pore sieving, and electrostatic interaction, were systematically summarized from the perspectives of adsorption thermodynamics and diffusion dynamics. Finally, the current drawbacks and challenges were highlighted and future opportunities were proposed for designing highly efficient MOF-based adsorbents for the adsorption of alkali metal ions.

This study leverages the implementation of China's pilot Emissions Trading Scheme (ETS) policies as a quasi-natural experiment. Utilizing panel data from 284 prefecture-level and above cities in China spanning 2003 to 2017, we employ a multi-period difference-in-differences (DID) approach to empirically investigate the carbon reduction effects, underlying mechanisms, and heterogeneity associated with the ETS pilots. The findings reveal that: (1) The ETS effectively reduces both the total carbon emissions and carbon intensity in the pilot regions through dual channels of aggregate control and efficiency improvement. This conclusion remains robust after a series of rigorous tests. (2) The ETS achieves carbon reduction in pilot cities through the combined action of market mechanisms and administrative interventions. While the role of market mechanisms is relatively limited, administrative interventions contribute significantly. (3) The carbon reduction effects of the ETS exhibit significant heterogeneity. From the perspective of urban resource endowment, non-resource-based cities outperform resource-based cities in carbon reduction effectiveness. Regarding allowance allocation methods, regions employing a hybrid approach combining free allocation with auction and fixed-price sales demonstrate superior carbon reduction outcomes compared to those using solely free allocation or a combination of free allocation and auction. In terms of carbon price, the ETS policy demonstrates greater effectiveness in reducing total carbon emissions in high-carbon-price regions than in low- and medium-carbon-price regions, but its effectiveness in reducing carbon intensity is weaker in high-price regions compared to low- and medium-price regions. This study provides empirical evidence and policy insights for China to tailor the implementation of its ETS to local conditions, thereby advancing the achievement of its "dual carbon" goals (carbon peaking and carbon neutrality) and fostering green transition development.

Public finance theory traditionally assumes the existence of a fiscal contract linking taxation, representation, and accountability. Communist regimes challenge this assumption by sustaining extensive public extraction and allocation without pluralistic taxation or political consent. This article develops the concept of fiscal control without a fiscal contract to analyze how public finance operates under central planning as a system of implicit extraction rather than as a policy instrument negotiated with taxpayers. Using Communist Albania (1945–1990) as a primary case study, the article adopts a qualitative, theory-driven approach and constructs an analytical framework structured around visibility of extraction, channels of coercion, fiscal discretion, accounting opacity, monetary control, and distributive effects. The analysis shows that a set of implicit extraction mechanisms—including administered prices, wage compression, constrained monetization, state monopolies, and the direct appropriation of production surpluses through centrally planned productive units—functioned as substitutes for explicit taxation, fulfilling allocative and stabilizing functions while suppressing fiscal visibility and accountability. A brief comparative reference to other socialist systems highlights Albania’s specificity as a case of extreme centralization and fiscal opacity. The article contributes to public finance scholarship by extending the analysis of fiscal systems beyond contractual settings and by conceptualizing budgets and accounting practices as instruments of governance rather than as arenas of collective choice.

This paper investigates an optimal decision-making and optimization framework for networked systems operating under the coupled effects of stochastic transmission delays, packet dropouts, and input delays, which is a critical unresolved challenge in data-driven intelligent systems deployed over shared communication networks. Such uncertainty-aware optimization problems exhibit strong similarities to modern recommender and decision support systems, where multiple performance criteria must be balanced under dynamic and resource-constrained environments while addressing the disruptive impact of coupled network-induced uncertainties. By explicitly modeling stochastic transmission delays and packet losses in the sensor to controller channel, together with input delays in the actuation loop, the problem is formulated as a stochastic optimal control task with multi-stage decision coupling that captures the interdependency of communication uncertainties and system performance. An optimal feedback policy is derived based on a discrete time Riccati recursion explicitly quantifying and mitigating the cumulative impact of network-induced uncertainties on the expected performance cost, which is a capability lacking in existing frameworks that treat uncertainties separately. Numerical simulations using realistic traffic models validate the effectiveness of the proposed framework. The results demonstrate that the proposed decision optimization approach offers a principled foundation for uncertainty-aware optimization with potential applicability to data-driven recommender and intelligent decision systems where coupled uncertainties and multi-criteria trade-offs are pervasive.

The article is devoted to the development of robust control algorithms for hybrid unmanned aerial vehicles of the helicopter type, for which the engines are five electric motors and an internal combustion engine. The current state of UAV control methods is analyzed with an emphasis on the features of the hybrid design, which requires adaptation to internal and external uncertainties. Such uncertainties include wind disturbance, change in the mass of the vehicle due to fuel consumption, and the inability to determine some parameters in real conditions. The improvement is based on basic algorithms developed and substantiated by the authors earlier. Such algorithms require specific information that makes their practical use impossible. It is proposed to improve the basic control algorithms by providing robust properties with respect to unknown parameters and numerical data processing technology. All algorithms are divided into three autopilots: altitude, course, and pitch and roll angles. They operate in each control cycle, regardless of the movement mode being performed. This ensures compensation of cross‒connections between control channels. The improved algorithms are comprehensively described in the form of block diagrams, which makes them suitable for practical implementation on board the UAV. Confirmation of the operability and determination of the effectiveness of the improved algorithms was carried out by modeling. The quality criterion was the discrepancy in the performance indicators between the basic and improved algorithms. Simulation of the flight of a hybrid UAV along a closed trajectory demonstrated the operability of the improved algorithms with some decrease in speed compared to the basic ones. Considering the practical orientation of the developed algorithms, this result is positive.

The paper considers the problem of compensation for unknown external disturbances for a class of linear stationary multidimensional systems with distinct input delays. It is assumed that external disturbances are harmonic signals with unknown frequencies, phases, amplitudes, and biases that simultaneously affect both the input and output of the system. To solve the problem, the direct disturbance compensation method based on the internal model principle is used in combination with the classical Falb-Wolovich linear state feedback decoupling method which allows increasing the convergence rate of output signals with a small adaptation parameter. In order to eliminate cross-interactions between control loops, the channel decoupling method based on Falb-Wolovich linear state feedback decoupling approach is applied to the system. Then, an observer is constructed to estimate the state vector of the external disturbance model and, based on the estimations, an adaptive control law with the memory regressor extension is designed to compensate for external disturbances based on the internal model principle. The system is stabilized simultaneously with the decoupling of control channels, which allows one to proceed to the problem of compensating for external unknown disturbances, bypassing the design phase of the stabilizing component of the control signal. There are no restrictions on the observability and stability of the control plant. An adaptive algorithm with the memory regressor extension combined with the Falb-Volovich linear state feedback decoupling method is proposed to compensate for unknown external disturbances for a class of linear stationary multidimensional systems with distinct control delays. The efficiency of the proposed approach is illustrated by an example of numerical simulation in the MATLAB/Simulink environment. The resulting transient response plots demonstrate that the proposed algorithm ensures the boundedness of all closed-loop signals and the asymptotic stability of the output variables in the presence of distinct input delays under external harmonic disturbances. The proposed approach allows obtaining an improved rate of convergence of processes and can be applied in engineering systems and complexes the mathematical description of which is given in the form of linear multidimensional systems with distinct input delays.

This paper develops a unified synchronization framework for octonion-valued fractional-order neural networks (FOOVNNs) subject to mixed delays, Lévy disturbances, and topology switching. A fractional sliding surface is constructed by combining I1−μeg with integral terms in powers of |eg|. The controller includes a nonsingular term −ρ2gsgc2sign(sg), a disturbance-compensation term −θ^gsign(sg), and a delay-feedback term −λgeg(t−τ), while dimension-aware adaptive laws ,CDtμρg=k1gN∥sg∥c2 and ,CDtμθ^g=k2gN∥sg∥ ensure scalability with network size. Fixed-time convergence is established via a fractional stochastic Lyapunov method, and predefined-time convergence follows by a time-scaling of the control channel. Markovian switching is treated through a mode-dependent Lyapunov construction and linear matrix inequality (LMI) conditions; non-Gaussian perturbations are handled using fractional Itô tools. The architecture admits observer-based variants and is implementation-friendly. Numerical results corroborate the theory: (i) Two-Node Baseline: The fixed-time design drives ∥e(t)∥1 to O(10−4) by t≈0.94s, while the predefined-time variant meets a user-set Tp=0.5s with convergence at t≈0.42s. (ii) Eight-Node Scalability: Sliding surfaces settle in an O(1) band, and adaptive parameter means saturate well below their ceilings. (iii) Hyperspectral (Synthetic): Reconstruction under Lévy contamination achieves a competitive PSNR consistent with hypercomplex modeling and fractional learning. (iv) Switching Robustness: under four modes and twelve random switches, the error satisfies maxt∥e(t)∥1≤0.15. The results support octonion-valued, fractionally damped controllers as practical, scalable mechanisms for robust synchronization under non-Gaussian noise, delays, and time-varying topologies.

This study addresses the synthesis of a combined control system using an asynchronous electric drive with vector control to compensate for control error and confirm the correctness of calculations using computer modeling. The advancement of microprocessor control systems for asynchronous motors has led to the widespread use of complete electric drives with vector control in metal-cutting machines. The vector control system of a frequency-controlled asynchronous electric drive significantly enhances transient response quality and expands the speed control range in high-precision electric drives. The synthesis of a servo tracking electric drive occurs under the condition that the input signal is determined by the content of the control program of the numerical control device and the law of movement of the working element is known. This mode is typical for feed drives in the contour processing mode. For a servo tracking electric drive of a lathe feed mechanism, one of the most common input signals is a signal changing at a constant speed. The value of the error in processing the input signal in the steady-state servo tracking mode has only a speed component, which depends on the rate of change of the input signal and the quality factor of the servo tracking electric drive. At a high rate of change of the input signal, the value of the speed error is significant. To compensate for the influence of the control signal magnitude on the value of the speed error, a combined control system must be implemented, and the transfer function of the correction device in the auxiliary control channel must be defined. The correction device transfer function calculated in the work ensures partial invariance of the system. The paper presents computer simulation results that confirming the operability and efficiency of the control system. Due to the use of combined control, partial invariance of the system is ensured and the value of the speed error is reduced by four times.

Carrier-based aircraft exhibit highly complex coupling characteristics across control channels. To address the control coupling issues encountered during carrier-based aircraft landing, as well as the undershoot phenomenon observed in trajectory adjustment, this paper proposes a direct longitudinal force control method based on the vertical translation mode. The proposed method enables decoupled control of altitude, velocity, and pitch channels while maintaining the aircraft's attitude stability. This approach simplifies the control logic and eliminates the undershoot phenomenon in trajectory adjustment. Furthermore, given the difficulty in acquiring the relative position between the aircraft and the carrier, a Kalman filter-based multi-source signal fusion method is introduced, which effectively suppresses the noise interference in radar signal acquisition by the carrier-based aircraft. Simulation results demonstrate that the proposed composite control method enables rapid trajectory tracking and enhances landing accuracy.

sEEG-based brain-computer interfacing in a large adult and pediatric cohort

The sixth generation (6G) of wireless communication systems is envisioned to be inherently AI-native, integrating intelligence into every network layer to support unprecedented capabilities, including terabit-per-second data rates, submillisecond latency, and pervasive sensing . This ambition re- quires managing extreme complexity introduced by revolutionary technologies such as Terahertz (THz) communication, Ultra- Massive MIMO (UM-MIMO), and Reconfigurable Intelligent Surfaces (RIS) . Machine Learning (ML) is recognized as the computational backbone for this transformation, enabling adaptive, self-optimizing, and context-aware wireless environments that fundamentally redefine how networks operate . This paper presents a systematic review, mapping ML across three progressive integration paradigms: AI for Network (AI4NET), Network for AI (NET4AI), and AI as a Service (AIaaS). We detail ML’s pivotal role in enhancing the physical layer through deterministic Wireless Environment Control (WEC) and robust channel estimation using generative models . Furthermore, we elaborate on distributed intelligence architectures, such as Federated Learning (FL) and Split Learning (SL), which are essential for balancing high computational demands with data privacy and resource constraints in the emerging Computing Power Network (CPN) . Finally, we argue that the core viability of 6G depends on embedding trustworthiness into its architecture, emphasizing the mandatory roles of Explainable AI (XAI) for operational accountability and Distributed Ledger Technology (DLT) for immutable data provenance .

This paper investigates the issue of secure state estimation of an islanded microgrid in an energy Internet scenario impacted by external disturbances, deception attacks and linear fractional uncertainty with the aid of attack-tolerant nonfragile control. Firstly, the observer system is developed through the measured output to estimate the dynamics of the considered system. Then, utilizing the information gleaned from the observer, a robust control is developed. Specifically, the proposed framework explicitly addresses simultaneous deception attacks occurring randomly in both the measurement and control channels. The consideration of attacks ensures that the secure assessment of states is shown while also accounting for the tolerance of attacks in the control channel. Subsequently, the convex optimization problem is set up via Lyapunov stability theory and Ito’s formula. Consequently, the gain matrices are reckoned, making sure that the system is finite-time stochastically bounded. Eventually, the theoretical insights are proficiently verified using numerical examples.

The control of chaotic and hyper-chaotic systems represents a crucial area of research in the field of nonlinear dynamic systems. In this study, we focus on applying chaos control techniques to a permanent magnet synchronous motor (PMSM), a system known to exhibit chaotic behavior under certain conditions. To achieve this, a sliding mode control (SMC) strategy integrated with a Lyapunov-based observer is proposed. The core concept involves designing a candidate Lyapunov function that governs the application of the control law, ensuring system stability while effectively suppressing chaotic dynamics. Through numerical simulations, the proposed sliding mode controller demonstrates its effectiveness in rapidly eliminating chaotic behavior and stabilizing the PMSM system toward a predefined reference trajectory. Notably, the system achieves error convergence within approximately 0.7 seconds under full control (four channels). When control channels are reduced to two, the system still maintains stability, showing flexibility and cost efficiency. In a further simulation, the chaotic PMSM is subjected to two unknown external disturbances, and the proposed controller continues to maintain stability with only a slight increase in convergence time. These quantitative results affirm the robustness, accuracy, and practicality of the proposed control method. This research confirms that integrating sliding mode control with a Lyapunov observer is an effective approach for chaos suppression in PMSMs, offering promising insights for the development of advanced control strategies in nonlinear electromechanical systems.

Amorphous oxide semiconductor (AOS) thin-film transistors (TFTs) based on In₂O₃ have garnered significant interest as pixel-switching elements for next-generation active-matrix liquid crystal displays (AMLCDs) and active-matrix organic light-emitting diode displays (AMOLEDs). The high field-effect mobility of In₂O₃-based AOS devices (10–25 cm²/V·s) offers substantial performance improvements over conventional amorphous silicon (a-Si) TFTs, which exhibit mobilities below 1 cm²/V·s. Additional advantages of AOS materials include low-temperature processability (room temperature to ~300 °C), isotropic wet etch characteristics, and high optical transparency (>85% in the visible regime), making them well-suited for large-area, flexible, and transparent electronics on cost-effective polymer substrates.Significant efforts have been made to integrate AOS materials into current-driven active-matrix displays, especially as pixel-driving switches in AMOLEDs. Strategies to enhance amorphous phase stability, suppress bias stress-induced threshold voltage (Vth) shifts, and control channel carrier concentrations have been widely explored and successfully implemented. One such strategy involves the incorporation of a third cation into binary oxide systems to suppress unintentional carrier generation. We recently reported that aluminum (Al) incorporation into InZnO enhances amorphous phase stability and carrier suppression, while simultaneously increasing carrier mobility, achieving Hall-effect mobilities up to ~45 cm²/V·s and field-effect mobilities up to ~20 cm²/V·s. These characteristics are considered critical for enabling ultra-high-definition display technologies.Post-deposition annealing is commonly used in AOS TFT fabrication, as it has been shown to improve field-effect mobility, enhance channel/contact interface quality, and reduce trap densities, thereby mitigating instability and hysteresis in device characteristics. However, annealing can also lead to a significant increase in channel carrier density, resulting in elevated off-state current and higher operation voltages. The underlying mechanisms behind this unintended carrier increase remain poorly understood.Furthermore, AOS-based TFTs are known to suffer from Vth instability under gate bias stress (positive or negative). Improving the reliability of these devices requires a deeper understanding of the mechanisms driving these instabilities.In this study, we investigate the origin of carrier density increases in InAlZnO (IAZO), a representative third-cation AOS system, following low-temperature (200 °C) annealing in air. Work function and bandgap analyses reveal that carrier densities increase by more than four orders of magnitude compared to unannealed IAZO. Photoelectron spectroscopy suggests that neither intrinsic (e.g., oxygen vacancies) nor extrinsic (e.g., cation substitution) doping mechanisms are primarily responsible. Instead, our results indicate that the carrier density increase is driven by the material’s tendency to equilibrate toward a higher carrier concentration in the absence of kinetic constraints. High-pressure oxidation experiments support this conclusion by demonstrating the inherently higher equilibrium carrier density of IAZO compared to typical TFT channel requirements.To further assess bias stability, we performed transfer characteristic measurements under prolonged gate bias stress. Under negative bias stress (NBS, –30 V), transfer curves remained largely unchanged, consistent with the depletion of channel carriers. However, under positive bias stress (PBS, +30 V), a notable positive shift in Vth was observed. Detailed analysis of field-effect mobility, subthreshold swing, Vth, and interfacial trap density was conducted to quantify this behavior.Overall, this combinatorial investigation provides insights into the origin of unintended carrier density increases and elucidates bias-induced instabilities in AOS materials, with implications for improving the performance and reliability of next-generation TFTs.This work was supported by the US National Science Foundation, Award numbers ECCS-1931088.References Reed, A. et al. Journal of Materials Chemistry C 2020, 8 (39), 13798-13810. (DOI: 10.1039/D0TC02655G)Lee, S. et al. Journal of Applied Physics 2011, 109 (6), 063702. (DOI: 10.1063/1.3549810)Reed, A. S. et al. Journal of Electronic Materials 2016, 45 (12), 6310-6316. (DOI: 10.1007/s11664-016-5008-1)Liu, M. et al. Journal of Electronic Materials 2022, 51 (4), 1813-1819. (DOI: 10.1007/s11664-022-09453-6)Liu, M. et al. Applied Surface Science 2021, 556, 149676. (DOI: 10.1016/j.apsusc.2021.149676)Lee, D. H. et al. ACS Applied Materials & Interfaces 2022, 14 (48), 53999-54011. (DOI: 10.1021/acsami.2c15719)Kim, H. et al. ACS Applied Nano Materials 2022, 5 (12), 17891-17899. (DOI: 10.1021/acsanm.2c03852)

This paper investigates, for the first time, the synthesis of a controller that incorporates a fractional-order integral component to achieve a closed-loop astaticism order greater than one. To enhance both static and dynamic accuracy, the controller integrates direct-signal-propagation neural networks within each control channel. The controlled plant is the BLDCM speed loop, which is modeled using a fractional-order differential equation. The study compares the performance of four controller types: a classical PID regulator tuned close to the optimal modulus criterion (IntPID); a fractional PI–PIμD controller (FrPID) that achieves an astaticism order of at least 1.8; and two hybrid neuro-controllers, NN–IntPID and NN–FrPID. While the FrPID controller reduces the root-mean-square error by nearly a factor of five compared with IntPID, the best results are delivered by NN–FrPID. Specifically, it decreases overshoot eight-fold during a reference step (from 2.98% to 0.35%), lowers the root-mean-square error during linear reference tracking by a factor of eleven, and reduces the relative speed error by more than thirty-five times. When combined with a fast learning algorithm executed at each control-cycle iteration, the controller enables the closed loop to adapt not only to variations in gain coefficients, but also to changes in the fractional-aperiodic order of the plant. These results demonstrate that neural fractional-integral controllers offer strong potential for improving accuracy and robustness in BLDC motor drives and are applicable to a wide range of electromechanical systems.

This article explores a sophisticated cyberattack method that bypasses technical defenses by targeting human psychology. Instead of attacking systems directly, this approach uses the Social Engineering Toolkit (SET) to create convincing deceptive campaigns that persuade users to inadvertently initiate the attack themselves. A key technical element involves generating a custom Meterpreter payload, which is programmed to establish a Reverse TCP connection. This creates a stealthy command and control channel by having the compromised system "call back" to an attacker-controlled server, granting remote access. Our work demonstrates how this powerful fusion of human manipulation and technical execution poses a significant threat that often evades conventional, technology-focused security measures.

Since the support struts of the helicopter main gearbox are the main transmission path for the excitation force from the main rotor to the fuselage, vibration transmission can be suppressed effectively by embedding actuators in the support struts. A new helicopter active vibration isolation system with hydraulic actuators embedded into the helicopter main gearbox support struts is proposed. A dynamic model of the helicopter active vibration isolation system with an eight-strut centralized configuration is established using the frequency response function synthesis method. Based on this model, the characteristics of vibration transmission in the system are analyzed. Considering the influence of coupling crosstalk of control channels, a feedback global multichannel (FGMC) adaptive controller based on the filtered-x least mean square (FxLMS) algorithm is proposed. The simulation and experimental results indicate that the convergence rate and stability of an FGMC controller are better than those of conventional decentralized multichannel (DMC) FxLMS controllers. In the experiment, the root-mean-square value attenuations are over 63% and 40% for the FGMC and DMC controllers, respectively. The experimental results of variable amplitude disturbing force further verify that the FGMC controller has strong convergence and robustness.