Search Results (122)

The dynamic model of a spatial flexible manipulator is challenging to establish accurately and is susceptible to disturbances from uncertainties, which negatively impact system control. To mitigate the effects of uncertainties and improve control precision, this paper develops the dynamic model of a spatial flexible manipulator system using the Lagrange principle and the assumed mode method. Additionally, to address the chattering issue in sliding mode control, a super-twisting algorithm sliding mode control strategy is proposed, which utilizes an RBF neural network to compensate for system uncertainties. By incorporating the concept of higher-order sliding mode, the super-twisting algorithm sliding mode is integrated with non-singular terminal sliding mode to design the sliding surface and control law, ensuring the stable and rapid convergence of the system state while reducing chattering. The simulation results demonstrate that the proposed strategy reduces fluctuations in rotation angle and angular velocity errors at both the input and output ends, enhances trajectory tracking performance, and achieves effective chattering suppression. Full article

This paper investigates the finite-region stability (FRS) and stabilization problems for 2D continuous-time systems described by a Roesser model. We first establish a novel set of FRS and finite-region boundedness (FRB) conditions, extending the ${\mathcal{L}}_2$-based concept on finite-time stability from 1D systems to the 2D continuous domain with a new condition based on the generalized state vector of the 2D continuous-time system in contrast with the norm-based condition found in the literature. Sufficient conditions are then derived to guarantee that the system state remains within a predefined quadratic region over a finite-time horizon. Furthermore, the framework is extended to analyze FRB under two distinct classes of external disturbances. Finally, a complete procedure for state feedback stabilization is provided, with all sufficient conditions for FRS and stabilization expressed entirely in terms of numerically tractable Linear Matrix Inequalities (LMIs) enabling controller design that ensures closed-loop finite-region performance under both disturbance classes. The effectiveness and feasibility of the proposed approach are demonstrated through numerical examples. Full article

This article proposes a conceptual reformulation of the phenomenon of boredom by carefully distinguishing ordinary situational or psychologically driven boredom from what is here termed existential boredom: a specific mode of disconnection in which the subject’s capacity to inhabit a meaningful horizon of possibilities becomes temporarily suspended. Rather than interpreting boredom as a mere lack of stimulation, momentary dissatisfaction, or simple emotional discomfort, the study argues that certain forms of boredom reveal a phenomenological contraction of possibility, involving disturbances in lived temporality, value orientation, imaginative projection, and embodied intentionality. Through a critical analysis of key thinkers, the article clarifies the tensions and limitations within classical accounts and delineates the proposed concept of existential boredom from adjacent phenomena such as Unheimlichkeit, Frankl’s “existential vacuum”, clinical apathy, and everyday boredom. It is argued that existential boredom functions as a phenomenological indicator of existential disconnection, not as an ontological diagnosis of meaninglessness but as a liminal experience that renders visible the temporary suspension of the structures that normally sustain meaningful world-disclosure. This conceptualisation also illuminates the contemporary prevalence of this affective state within contexts of hyper-stimulation, attentional fragmentation, and the erosion of meaning frameworks. By offering an integrated analytical framework, the article contributes to a more rigorous understanding of boredom as an existential phenomenon, with implications for philosophy, contemporary psychology, and the study of human experience in modern life. This approach not only expands our understanding of boredom but also invites us on a journey of self-discovery and personal growth. Full article

The way in which Aquaporin-4 (AQP4) is localized on the astrocytes’ surface—i.e., with AQP4 channels predominantly located on the endfeet of astrocytes near the blood vessels—represents an important structural element for maintaining brain fluid homeostasis. In addition to this structural function, AQP4 polarity also facilitates glymphatic transport, the maintenance of the blood–brain barrier (BBB) functions, ion buffering, and neurotransmitter removal, and helps regulate neurovascular communications. The growing body of literature suggests that the loss of AQP4 polarity—a loss in the organization of AQP4 channels to the perivascular membrane—is associated with increased vascular, inflammatory, and metabolic disturbances in the context of many neurological diseases. As a result, this review attempts to synthesize both experimental and clinical studies to highlight that AQP4 depolarization often occurs in conjunction with early signs of neurodegeneration and neuroinflammation; however, we are aware that the loss of AQP4 polarity is only one factor in a complex pathophysiological environment. This review examines the molecular structure responsible for maintaining the polarity of AQP4—such as dystrophin–syntrophin complexes, orthogonal particle arrays, lipid microdomains, trafficking pathways, and transcriptional regulators—and describes how the vulnerability of these systems to various types of vascular stress, inflammatory signals, energy deficits, and mechanical injury can lead to a loss of AQP4 polarity. Furthermore, we will explore how a loss of AQP4 polarity can lead to the disruption of perivascular fluid movement, changes in blood–brain barrier morphology, enhanced neuroimmune activity, changes in ionic and metabolic balance, and disruptions in the global neural network synchronization. Importantly, we recognize that each of these disruptions will likely occur in concert with other disease-specific mechanisms. Alterations in AQP4 polarity have been observed in a variety of neurological disorders including Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, traumatic brain injury, and glioma; however, we also observe that the same alterations in fluid regulation occur across all of these different diseases, but that no single upstream event accounts for the alteration in polarity. Ultimately, we will outline emerging therapeutic avenues to restore perivascular fluid transport, and will include molecular-based therapeutic agents designed to modify the anchoring of AQP4, methods designed to modulate the state of astrocytes, biomaterials-based drug delivery systems, and therapeutic methods that leverage dynamic modulation of the neurovascular interface. Future advances in multi-omic profiling, spatial proteomics, glymphatic imaging, and artificial intelligence will allow for earlier identification of AQP4 polarity disturbances and potentially allow for the development of more personalized treatment plans. Ultimately, by linking these concepts together, this review aims to frame AQP4 polarity as a modifiable aspect of the “fluidic connectome”, and highlight its importance in maintaining overall brain health across disease states. Full article

Rapid and precise intervention in disaster and medical-aid scenarios demands aerial platforms that can both survey and physically interact with their environment. This study presents the design, fabrication, modeling, and experimental validation of a one-piece, 3D-printed quadcopter with an integrated six-degree-of-freedom aerial manipulator robotic arm tailored for emergency response. First, we introduce an ‘X’-configured multi-rotor frame printed in PLA+ and optimized via variable infill densities and lattice cutouts to achieve a high strength-to-weight ratio and monolithic structural integrity. The robotic arm, driven by high-torque servos and controlled through an Arduino-Pixhawk interface, enables precise grasping and release of payloads up to 500 g. Next, we derive a comprehensive nonlinear dynamic model and implement an Extended Kalman Filter-based sensor-fusion scheme that merges Inertial Measurement Unit, barometer, magnetometer, and Global Positioning System data to ensure robust state estimation under real-world disturbances. Control algorithms, including PID loops for attitude control and admittance control for compliant arm interaction, were tuned through hardware-in-the-loop simulations. Finally, we conducted a battery of outdoor flight tests across spatially distributed way-points at varying altitudes and times of day, followed by a proof-of-concept medical-kit delivery. The system consistently maintained position accuracy within 0.2 m, achieved stable flight for 15 min under 5 m/s wind gusts, and executed payload pick-and-place with a 98% success rate. Our results demonstrate that integrating a lightweight, monolithic frame with advanced sensor fusion and control enables reliable, mission-capable aerial manipulation. This platform offers a scalable blueprint for next-generation emergency drones, bridging the gap between remote sensing and direct physical intervention. Full article

This paper investigates a distributed robust tracking control method with prescribed convergence time for heterogeneous air–ground multi-agent systems under the combined effects of deception attacks and actuator faults. Considering the corruption of state information caused by attacks, a time-varying constraint function is first designed, and a command filtering mechanism is introduced. Through coordinate transformation, the disturbed state is indirectly estimated and safely fed back. To cope with actuator malfunctions leading to uncertain control effectiveness, a rationally designed adaptive law is developed for real-time identification and compensation of such uncertainties. Furthermore, within the backstepping control framework, the concept of time-varying constraints is integrated to propose an adaptive prescribed-time controller, transforming the tracking control problem into an error constraint form, thereby ensuring the system error converges within a specified range within a given time. To reduce communication load, the controller is implemented with an event-triggered mechanism, where control signals are updated only at trigger times, effectively avoiding Zeno behavior. Finally, the boundedness and stability of the closed-loop system are proven using Lyapunov methods. Simulation results demonstrate that this control strategy maintains stable and rapid heterogeneous formation tracking performance even in the presence of deception attacks and actuator faults. Full article

Recent technological advances in communication networks, intelligent devices, power electronics, and phasor measurement units have significantly transformed the operation of modern power systems. This evolution gave rise to smart grids, which enable the flow of real-time information on the operational state of the grid and of control commands across multiple communication infrastructures, using a variety of protocols and standards, between control centers and devices deployed throughout the grid’s physical structure. At the same time, it has exposed power systems to new challenges and threats, due to the vulnerabilities inherited from the different components they integrate. Attackers have a variety of attacks at their disposal, by which they can disturb the availability of electricity as well as cause damage to the smart grid’s physical structure. Therefore, cybersecurity has become an important aspect of the smart grid concept. This field of research has attracted the attention of many researchers, and in the last decade or so, the number of studies on the cyber-physical security of smart grids has surged significantly. Proportionally, an important number of survey papers were published as well. It has therefore become more difficult to navigate literature on the topic of smart grid cyber-physical security due to the large number of papers, the complexity of the grid’s structure, and the variety of attacks, resolution methods, and techniques. To address this issue, in this work, we present a comprehensive review of existing literature reviews on the topic of smart grid cyber-physical security. We reviewed 100 survey papers, which were categorized into general surveys, attack-specific surveys, method-specific surveys, and component-specific surveys. We discussed and highlighted research tendencies in terms of attacks and methods used to protect smart grids. Additionally, we presented an overview of the different research challenges and possible future directions. Full article

In micro-production technology (MPT), the demand for ultra-precise machine tools has been steadily increasing. Conventional guideway systems, such as hydrostatic or aerostatic bearings, often face limitations in terms of compactness, media supply, and susceptibility to external disturbances, which restrict their applicability in next-generation precision manufacturing. In order to address these challenges, this paper presents a novel media-free, contactless, and active three-degree-of-freedom (DOF) planar positioning (guiding) system that integrates ultrasonic actuators with electromagnetic actuators. The hybrid concept combines the high load capacity and self-stabilization of double-acting ultrasonic actuators and pronounced controllability of the electromagnetic actuators. A prototype system was developed and experimentally validated. Ultrasonic actuators successfully established a stable levitation state, while electromagnetic actuators provided fine adjustment of the levitation height in the micrometer range. Load tests demonstrated that the system maintained stable levitation under an external load of 30 N. These results confirm the feasibility of the proposed approach for robust and precise positioning. The developed hybrid system therefore represents the potential for next-generation precise manufacturing machines in MPT, offering high accuracy and robustness against external disturbances. Full article

The increased penetration of power electronics interfaced resources in modern power systems is unlocking new opportunities and challenges. New concepts like multiport converters can further enhance the efficiency and power density of power electronics-based solutions. The triple active bridge is an isolated multiport converter with soft switching and high voltage gain that can integrate different sources, storage, and loads, or act as a building block for modular systems. However, the triple active bridge suffers from power flow cross-coupling, which affects its dynamic performance if it is not removed or mitigated. Unlike the extensive literature on two-port power converters, studies on modeling and control comparison for multiport converters are still lacking. Therefore, this paper presents and compares different modeling and decoupling control approaches applied to the triple active bridge converter, highlighting their benefits and limitations. The converter operation and modulation are introduced, and modeling and control strategies based on the single phase shift power flow control are detailed. The switching model, generalized full-order average model, and the reduced-order model derivations are presented thoroughly, and a comparison reveals that first harmonic approximations can be detrimental when modeling the triple active bridge. Furthermore, the model accuracy is highly sensitive to the operating point, showing that the generalized average model better represents some dynamics than the lossless reduced-order model. Furthermore, three decoupling control strategies are derived aiming to mitigate cross-coupling effects to ensure decoupled power flow and improve system stability. To assess their performance, the TAB converter is subjected to power and voltage disturbances and parameter uncertainty. A comprehensive comparison reveals that linear PI controllers with an inverse decoupling matrix can effectively control the TAB but exhibit large settling time and voltage deviations due to persistent cross-coupling. Furthermore, the decoupling matrix is highly sensitive to inaccuracies in the converter’s model parameters. In contrast, linear active disturbance rejection control and sliding mode control based on a linear extended state observer achieve rapid stabilization, demonstrating strong decoupling capability under disturbances. Furthermore, both control strategies demonstrate robust performance under parameter uncertainty. Full article

The cooperative flight of manned and unmanned aerial vehicles (MAV/UAVs) has recently become a focus in the research of civilian and humanitarian fields, in which formation control is crucial. This paper takes the improvement of convergence performance and resource conservation as the entry point to study control problems of cooperative formation configuration of MAV/UAVs. Following the backstepping recursive design procedures, an event-triggered fixed-time formation control strategy for MAV/UAVs operating under modeling uncertainties and external disturbances is presented. Moreover, a novel switching threshold event-triggered mechanism is introduced, which dynamically adjusts control signal updates based on system states. Compared with periodic sampling control (Controller 1), fixed threshold strategies (Controller 2) and relative threshold strategies (Controller 3), this mechanism enhances resource efficiency and prevents Zeno behavior. On the basis of Lyapunov stability theory, the closed-loop system is shown to be stable in the sense of the fixed-time concept. Numerical simulations are carried out in Simulink to validate the effectiveness of the theoretical findings. The results show that compared with the three comparison methods, the proposed control method saves 86%, 34%, and 43% of control transmission burden respectively, which significantly reduces the number of triggered events. Full article

In this study, a novel comparison between single phase 7-Level Packed U—Cell (PUC) inverter and single phase 9-Level Cross Switches Cell (CSC) inverter with Model Predictive Controller (MPC) for solar grid-tied applications is presented. Our innovation introduces a unique approach by integrating PV solar panels in PUC and CSC inverters in their two DC links rather than just one which increases power density of the system. Another key benefit for the proposed models lies in their simplified design, offering improved power quality and reduced complexity relative to traditional configurations. Moreover, both models feature streamlined control architectures that eliminate the need for additional controllers such as PI controllers for grid reference current extraction. Furthermore, the implementation of Maximum Power Point Tracking (MPPT) technology directly optimizes power output from the PV panels, negating the necessity for a DC-DC booster converter during integration. To validate the proposed concept’s performance for both inverters, extensive simulations were conducted using MATLAB/Simulink, assessing both inverters under steady-state conditions as well as various disturbances to evaluate its robustness and dynamic response. Both inverters exhibit robustness against variations in grid voltage, phase shift, and irradiation. By comparing both inverters, results demonstrate that the CSC inverter exhibits superior performance due to its booster feature which relies on generating voltage level greater than the DC input source. This primary advantage makes CSC a booster inverter. Full article

In this paper, the concept of symmetry is utilized to design active fault tolerant trajectory-tracking control of autonomous distributed-drive electric vehicles—that is, the construction and the solution of active fault tolerant trajectory-tracking controllers are symmetrical. This paper presents a hierarchical fault tolerant control strategy for improving the trajectory-tracking performance of autonomous distributed-drive electric vehicles (ADDEVs) under steer-by-wire (SBW) system failures. Since ADDEV trajectory dynamics are inherently affected by complex traffic conditions, various driving maneuvers, and other road environments, the main control objective is to deal with the ADDEV trajectory-tracking control challenges of system uncertainties, SBW failures, and external disturbance. First, the differential steering dynamics model incorporating a 3-DOF coupled system and stability criteria based on the phase–plane method is established to characterize autonomous vehicle motion during SBW failures. Then, by integrating cascade active disturbance rejection control (ADRC) with Karush–Kuhn–Tucker (KKT)-based torque allocation, the active fault tolerant control framework of trajectory tracking and lateral stability challenges caused by SBW actuator malfunctions and steering lockup is addressed. The upper-layer ADRC employs an extended state observer (ESO) to estimate and compensate against uncertainties and disturbances, while the lower-layer utilizes KKT conditions to optimize four-wheel torque distribution to compensate for SBW failures. Simulations validate the effectiveness of the controller with serpentine and double-lane-change maneuvers in the co-simulation platform MATLAB/Simulink-Carsim^® (2019). Full article

This study investigates the trajectory-tracking control problem of a two-degree-of-freedom underwater manipulator operating in a complex disturbance environment. A dynamic model of the multi-link serial manipulator is first established. In this study, water resistance and additional mass forces acting on the manipulator are analyzed and calculated using differential analysis and the Morrison formula. To account for coupling between joints, the concept of equivalent gravity is introduced to precisely calculate the underwater manipulator’s buoyancy and gravity. As a result, a relatively accurate dynamic model of the underwater manipulator is established. To mitigate the influences of external disturbances and unmodeled parts on the manipulator, a finite-time extended state observer (FTESO) is designed to estimate system quantities that are difficult to measure directly. The robustness of the controller is enhanced using a feedforward compensation mechanism, and it is demonstrated that the observation error of the observer converges in finite time. Finally, a global fast terminal sliding mode controller (GFTSMC) is developed for trajectory tracking, integrated with the aforementioned observer, and designed to smooth and limit the controller’s output. The controller’s stability is proven using Lyapunov stability theory, and its effectiveness is verified through simulation-based comparison experiments. Full article

This paper investigates the problem of robust stability for a class of switched interconnected systems characterized by unstable modes, time-varying state delays, and switching uncertainties. Switching uncertainties cause variations in both the nominal switching sequence and the prescribed switching instants, which significantly impact system stability. To address this, two novel concepts—namely, the generalized nominal switching signal and the composite switching signal—are introduced under switching uncertainty disturbances. Additionally, a new index, termed the generalized mode changing rate, is proposed. Building on these concepts and index, the idea of the vector Lyapunov function and average dwell time method are further incorporated to derive sufficient conditions for ensuring the robust exponential stability of the system. Finally, a numerical example is provided to demonstrate the effectiveness and validity of the proposed theory. Full article

Offshore wind farms play a pivotal role in the global transition to clean energy but remain susceptible to diverse meteorological hazards—ranging from highly variable wind speeds and temperature anomalies to severe oceanic disturbances—that can jeopardize both turbine safety and overall power output. Although Hidden Markov Models (HMMs) have a longstanding track record in operational forecasting, this study leverages and extends their capabilities by introducing a dynamic HMM framework tailored specifically for multi-risk offshore wind applications. Building upon historical datasets and expert assessments, the proposed model begins with initial transition and observation probabilities and then refines them adaptively through periodic or event-triggered recalibrations (e.g., Baum–Welch), thus capturing evolving weather patterns in near-real-time. Compared to static Markov chains, naive Bayes classifiers, and RNN (LSTM) baselines, our approach demonstrates notable accuracy gains, with improvements of up to 10% in severe weather conditions across three industrial-scale wind farms. Additionally, the model’s minutes-level computational overhead for parameter updates and state decoding proves feasible for real-time deployment, thereby supporting proactive scheduling and maintenance decisions. While this work focuses on the core dynamic HMM method, future expansions may incorporate hierarchical structures, Bayesian uncertainty quantification, and GAN-based synthetic data to further enhance robustness under high-dimensional measurements and rare, long-tail meteorological events. In sum, the multi-risk forecasting methodology presented here—though built on an established HMM concept—offers a practical, adaptive solution that significantly bolsters safety margins and operational reliability in offshore wind power systems. Full article