Search Results (464)

Digital chaotic systems suffer severe dynamical degradation under finite computational precision, compromising their randomness and unpredictability in security-critical applications. To address this challenge, we introduce the High-Dimensional Delayed Cyclic-Coupled Chaotic Model (HD-DCCCM), a unified framework that integrates high-dimensional coupling, delayed feedback, and time-varying parameter control. In this synergistic design, dynamic perturbations from delays and time-varying signals continuously excite the high-dimensional structure, effectively preventing the collapse into short periodic orbits typical of low-precision environments. Systematic numerical analyses confirm that the HD-DCCCM generates stable hyperchaos with significantly extended periods, consistently outperforming classical maps and representative anti-degradation methods. Moreover, the framework demonstrates strong robustness and flexibility across both homogeneous (identical maps) and heterogeneous (hybrid maps) configurations. These results position the HD-DCCCM as a general and powerful paradigm for constructing degradation-resilient chaotic systems, with broad potential for next-generation secure communications and cryptographic applications. Full article

This paper presents a unified framework for high-precision dynamic target tracking that combines phase-map-based visual servoing with Model Predictive Control (MPC). Phase maps obtained from fringe projection provide dense, subpixel geometric feedback, enabling accurate end-effector velocity computation; however, their high dimensionality leads to substantial computational overhead that hinders real-time control. To overcome this limitation, we introduce a phase-map-specific dimensionality reduction strategy that constructs a low-dimensional control subspace through gradient-guided sparsification and PCA embedding while preserving the controllability of the original interaction model. An adaptive prediction horizon is further developed to regulate MPC complexity according to the rate of phase variation, enabling real-time deployment without compromising tracking accuracy. In addition, an Extended Kalman Filter (EKF) is integrated into the control loop to compensate for system delays and improve trajectory prediction in dynamic scenarios. Experimental results on multi-axis robotic manipulation demonstrate that the proposed approach achieves superior tracking accuracy and computational efficiency compared with conventional visual servoing methods, validating the feasibility of phase-map-driven predictive control for high-speed dynamic target tracking. Full article

Background: Moral development (MD) arises from the interaction of attachment, temperament, emotion regulation, and decision-making. Children and adolescents with Attention-Deficit/Hyperactivity Disorder (ADHD) frequently show impairments across these domains, suggesting increased vulnerability to disruptions in MD. However, the mechanisms linking ADHD to MD remain poorly understood. Methods: A systematic review was conducted according to PRISMA 2020 guidelines. PubMed was searched for studies published between January 2014 and November 2024 examining MD-related constructs, including moral reasoning, fairness, aggression, bullying, callous–unemotional (CU) traits, decision-making, and reward sensitivity, in individuals aged 0–18 years with diagnosed or subclinical ADHD. Due to substantial heterogeneity in study design, measures, and outcomes, a qualitative synthesis was performed. Results: Of the 2104 records identified, 23 studies met inclusion criteria. Insecure or disorganized attachment, difficult temperament, and emotional dysregulation consistently emerged as developmental risk factors for impaired MD. Hyperactivity–impulsivity and deficient inhibitory control were strongly associated with aggressive and antisocial behaviors. Children with ADHD demonstrated a pronounced preference for immediate over delayed rewards, altered decision-making in social contexts, and reduced sensitivity to positive feedback. CU traits and aggression were frequently identified as behavioral correlates of MD impairments, particularly in interaction with family adversity and comorbid externalizing conditions. Social dysfunction, including bullying involvement, peer rejection, and interpersonal difficulties, was common and contributed to elevated long-term psychosocial risk. Conclusions: ADHD is associated with multidimensional vulnerabilities in MD through intertwined cognitive, emotional, and relational pathways. Interventions targeting attachment security, emotion regulation, reward processing, and social skills may foster MD and reduce later social difficulties. Longitudinal and cross-cultural research is needed to clarify causal mechanisms and inform developmentally sensitive prevention and treatment strategies. Full article

This paper concentrates on fractional-order fuzzy cellular neural networks (FOFCNNs) with bounded uncertainties and transmission delays. To better capture real-world dynamic behaviors, the fuzzy AND and OR operators are employed to construct drive-response systems. For the fixed-time synchronization task of the systems, a novel multi-module feedback controller incorporating three functional terms is designed. These terms aim to eliminate delay effects, ensure fixed-time convergence, and reduce parameter conservativeness. Leveraging the properties of fractional-order operators and our multi-module control scheme, new synchronization criteria of the studied drive-response systems can be established within a predefined time. An upper bound on the settling time is derived, depending on the system size and control parameters, but independent of the initial conditions. A significant corollary is derived for the case of no uncertainties under the nonlinear controller. Numerical experiments discuss the impact of uncertainties and delays on synchronization, and confirm the validity of the results presented in this study. Full article

This paper aims to address the data-driven feedback control problem of Delta fractional-order systems. First, we convert the studied system into an integer-order discrete-time system with progressively increasing time delays. A novel stability condition is then established for the resulting system. Leveraging this stability condition and a data-driven approach, we proceed to analyze the design of both the state feedback controller and the guaranteed-cost data-driven feedback controller, utilizing state data and input–output data, respectively. Finally, we demonstrate the effectiveness of our obtained data-driven control strategies through two simulation examples. Full article

The global space economy, valued at approximately USD 400–630 billion (depending on definitional scope), is projected to expand rapidly, crossing USD 1 trillion as early as 2032 and reaching up to about USD 1.8 trillion by 2035. This growth has been driven by a surge (a roughly twelvefold increase) in satellite launches over the past decade, transforming Earth’s orbits into an increasingly congested domain plagued by space debris. The proliferation of space junk poses an escalating threat to orbital sustainability, yet effective governance mechanisms remain limited. This paper examines why conventional solutions for managing common-pool resources (command-and-control regulation, Pigouvian taxes, private property rights, allocation of tradable permits, and horizontal governance regimes) are not fully effective or are difficult to implement in addressing the orbital debris problem. Using a system dynamics perspective, the study qualitatively maps hypothesized feedback mechanisms shaping orbital expansion and space debris accumulation. It suggests that, under the assumed causal structure, reinforcing growth loops associated with geopolitical rivalry and commercial cost reductions linked to the New Space paradigm currently dominate over delayed balancing effects arising from the finite nature of orbital space, whose regenerative capacity is progressively degraded. There exists a threshold of exploitation beyond which orbital space effectively behaves as a non-renewable resource. The analysis suggests that, without binding international coordination, meaningful intervention may require the occurrence of a catalyzing crisis—e.g., a localized cascade of orbital object collisions that could transform stakeholder perceptions and enables active debris removal deployment. Full article

This study explores finite-time synchronization (FTS) in fractional-order quaternion-valued neural networks (FQVNNs) characterized by discontinuous activation functions and uncertainties in parameters. Initially, leveraging the properties of the Mittag-Leffler function along with fractional-order (F-O) delayed differential inequalities, a novel finite-time stability theorem for F-O systems is established, building upon previous research findings. Next, based on norm definitions, two state feedback controllers employing quaternion 1-norm and quaternion 2-norm are devised to ensure FTS for the system under consideration. Following this, by utilizing differential inclusion theory, examining the quaternion sign function, employing advanced inequality methods, applying principles of F-O differential equations, and using the Lyapunov functional approach, new criteria for achieving FTS in FQVNNs are formulated. Additionally, precise estimates for the settling time are presented. In conclusion, two carefully designed numerical examples are included to corroborate the theoretical results derived. Full article

The increasing proliferation of new energy vehicles and autonomous vehicles has led to the formation of mixed traffic flows characterized by diverse driving behaviors, posing new challenges for intersection signal control. To address this issue, this study proposes a multi-class customer feedback queuing network (MCFFQN) model that incorporates state-dependent road capacity and congestion propagation mechanisms to accurately capture the stochastic and dynamic nature of mixed traffic flows. An evaluation framework for intersection performance is established based on key indicators such as vehicle delay, the energy consumption of new energy vehicles, and the fuel consumption and emissions of conventional vehicles. A recursive solution algorithm is developed and validated through simulations under various traffic demand scenarios. Building on this model, a signal timing optimization model aimed at minimizing total costs—including delay and environmental impacts—is formulated and solved using the Mesh Adaptive Direct Search (MADS) algorithm. A case study demonstrates that the optimized signal timing scheme significantly enhances intersection performance, reducing vehicle delay, energy consumption, fuel consumption, and emissions by over 20%. The proposed methodology provides a theoretical foundation for sustainable traffic management under mixed traffic conditions. Full article

At present, generative AI has problems of insufficient content generation accuracy, weak personalized response, and low reasoning efficiency in adaptive learning scenarios, which limit its in-depth application in intelligent teaching. To solve this problem, this paper proposed a Transformer fine-tuning method based on low-rank adaptation technology, which realized efficient parameter update of pre-trained models through low-rank matrix insertion, and combined the instruction fine-tuning strategy to perform domain adaptation training on the model for the constructed educational scenario dataset. At the same time, a dynamic prompt construction mechanism was introduced to enhance the model’s context perception ability of individual learners’ behaviors, thereby achieving precise alignment and personalized control of generated content. This paper embeds the “wrong question guidance” and “knowledge graph embedding” mechanisms in the model, provides intelligent feedback based on student errors, and promotes in-depth understanding of subject knowledge through knowledge graphs. Experimental results showed that this method scored higher than 0.9 in BLEU and ROUGE-L. The average response delay was low, which was significantly better than the traditional fine-tuning method. This method showed good adaptability and practicality in the fusion of generative AI and adaptive learning and provided a generalizable optimization path and application solution for intelligent education systems. Full article

This paper examines an optimal investment–consumption problem in a setting where the financial environment is influenced by both stochastic factors and delayed effects. The investor, endowed with Constant Relative Risk Aversion (CRRA) preferences, allocates wealth between a risk-free asset and a single risky asset. The short rate follows a Vas$\check{\mathrm{i}}$ček-type term structure model, while the risky asset price dynamics are driven by a delayed Heston specification whose variance process evolves according to a Cox–Ingersoll–Ross (CIR) diffusion. Delayed dependence in the wealth dynamics is incorporated through two auxiliary variables that summarize past wealth trajectories, enabling us to recast the naturally infinite-dimensional delay problem into a finite-dimensional Markovian framework. Using Bellman’s dynamic programming principle, we derive the associated Hamilton–Jacobi–Bellman (HJB) partial differential equation and demonstrate that it generalizes the classical Merton formulation to simultaneously accommodate delay, stochastic interest rates, stochastic volatility, and consumption. Under CRRA utility, we obtain closed-form expressions for the value function and the optimal feedback controls. Numerical illustrations highlight how delay and market parameters impact optimal portfolio allocation and consumption policies. Full article

This paper addresses stability issues, including voltage fluctuation, a frequency offset, and broadband oscillation resulting from the high penetration of renewable energy in a photovoltaic high-permeability distribution network. This paper proposes an active support control strategy which is energy storage grid-connected based on a virtual synchronous generator (VSG). This strategy endows the energy storage system with virtual inertia and a damping capacity by simulating the rotor motion equation and excitation regulation characteristics of the synchronous generator, and effectively enhances the system’s ability to suppress power disturbances. The small-signal model of the VSG system is established, and the influence mechanism of the virtual inertia and damping coefficient on the system stability is revealed. A delay compensator in series with a current feedback path is proposed. Combined with the damping optimization of the LCL filter, the instability risk caused by high-frequency resonance and a control delay is significantly suppressed. The novelty lies in the specific configuration of the compensator within the grid–current feedback loop and its coordinated design with VSG parameters, which differs from traditional capacitive–current feedback compensation methods. The experimental results obtained from a semi-physical simulation platform demonstrate that the proposed control strategy can effectively suppress voltage fluctuations, suppress broadband oscillations, and improve the dynamic response performance and fault ride-through capability of the system under typical disturbance scenarios such as sudden illumination changes, load switching, and grid faults. It provides a feasible technical path for the stable operation of the distribution network with a high proportion of new energy access. Full article

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. Full article

In this paper, we investigate a semilinear parabolic friction system with time-delay feedback control and diffusion. This model more accurately describes the coupled dynamic behavior between vibrations induced by time-delayed control forces and the diffusion-driven evolution of material surface properties in practical friction processes. Through eigenvalue analysis, it is proven that the system’s stability does not vary monotonically with parameters. Instead, as the time delay varies, the system undergoes a finite number of alternating switches between stability and instability, before eventually losing its stability. The established stability criteria and bifurcation formulae can provide a predictive basis for and strategies to avoid the frictional vibration caused by time-delayed feedback in mechanical systems, providing significant guidance for vibration-reducing design and control parameter optimization in equipment such as braking systems and precision machine tools. Full article

This paper focuses on finite-horizon optimum state feedback control problems for interconnected systems of two players involved with asymmetric one-step delay information. For the finite horizon optimum decentralized control problem, a crucial and adequate condition is derived by using Pontryagin’s maximum principle. Under this framework, player 1 transmits its state and control input data with a one-step delay to the controller of player 2, while player 1’s controller does not have access to the real-time or delayed states and control inputs of player 2, resulting in an asymmetric information structure characterized by a one-step delay Then, the solutions to the forward and backward stochastic difference equations are derived. A target tracking system is given in numerical examples to verify the proposed algorithm. Full article

With the rapid development of the low-altitude economy, UAV logistics delivery systems have garnered widespread attention due to their flexibility and efficiency. The cooperative delivery mode involving a UAV with a suspended payload and a ground vehicle represents a typical networked distribution scenario, whose performance is constrained by the tight coupling of sensing, communication, and control. In practical applications, sensor measurement noise and sudden disturbances propagate through the closed-loop system, severely degrading velocity synchronization and swing angle stability. To address this challenge, this paper focuses on a quadrotor UAV slung-load system and proposes a three-layer nested networked closed-loop control architecture for simultaneous velocity tracking of a moving ground target and swing angle stabilization. First, by establishing the system’s dynamic model, the mapping relationship between cable tension and the payload swing angle (based on sensor feedback) is revealed. Then, by setting the payload velocity as the outermost control objective and constructing a coupled error to drive a virtual swing angle actuator, the direct impact of noise in the raw sensor data is effectively mitigated. Subsequently, the desired acceleration of the UAV is derived through inverse computation, achieving synchronous optimization of velocity tracking and swing angle suppression. Theoretical analysis using Lyapunov methods demonstrates the stability of the closed-loop system in the presence of bounded delays. Simulation results show that the proposed method effectively suppresses payload swing, controls velocity synchronization error, and exhibits strong robustness against sensor noise and sudden disturbance. This study provides a control solution that improves the precision and robustness of sensor-enabled networked control systems in complex dynamic scenarios Full article