Search Results (39)

A mixed redundancy, in which some components of a subsystem operate under active redundancy while the others wait in cold standby, has recently been shown to achieve a higher system reliability than the traditional active and standby strategies within equivalent resources. Existing reliability models for the strategy, however, either provide only a lower bound of the subsystem reliability or restrict the components to be identical with exponential or Erlang lifetimes. This study proposes an exact reliability model for a mixed redundant system composed of heterogeneous components. The time to failure of every candidate component is described by a generalized phase-type distribution (PHD), and a structured continuous-time Markov chain (CTMC) integrates the active redundant module, the standby components—installed in a specified sequence recorded by an ordered component-index set—and an imperfect fault detector/switch. Because the model yields the infinitesimal generator of the subsystem lifetime, it provides not only the exact reliability but also the hazard function and the moments of the system lifetime. Building on a pre-computed database that enumerates every feasible subsystem structure, the redundancy allocation problem determining the component types, their number and sequence, and the number of active redundancies is formulated as a compact binary integer linear program and solved to optimality. Benchmark experiments show that the previous approximate function underestimates the achievable design by an average maximum possible improvement (MPI) of 15.6%, and that permitting heterogeneous components raises the optimal system reliability by a further 7.3% on average—up to 18.7% as the resource budget grows. Full article

Accurate prediction of the cracking moment in RC beams with openings is crucial for structural safety assessment. This study systematically establishes, for the first time, theoretical prediction models for the cracking moment of six categories of beams with openings and their corresponding applicability criteria. The reliability of the models was validated via ABAQUS finite element analysis, comparing GB 50010-2010 and ACI 318-19 codes. The results indicate that the theoretical model predictions align well with numerical simulation results, exhibiting low dispersion (central openings: Mean = 0.9903, CV = 0.0295; eccentric openings: Mean = 1.0078, CV = 0.032). Model predictions also show high consistency with calculations from the GB 50010-2010 code formulas (central openings: Mean = 1.0157, CV = 0.0471; eccentric openings: Mean = 1.0026, CV = 0.0563). However, agreement with ACI 318-19 code formula calculations is lower and dispersion is greater (central openings: Mean = 1.1858, CV = 0.1068; eccentric openings: Mean = 1.174, CV = 0.1009), primarily attributable to the conservative nature of the ACI 318-19 code. The study reveals that the theoretical models for the cracking moment of beams with central and eccentric openings are not unique and dynamically switch based on changes in material parameters and opening parameters. For beams with central openings, the opening ratio threshold was identified as 30%, and a maximum opening ratio not exceeding 48% is recommended. For beams with eccentric openings, the optimal eccentricity for different opening ratios was determined to be 8%. This research provides a theoretical basis and technical support for the universal prediction of cracking performance in beams with openings. Full article

Four-wheel-drive electric vehicles equipped with independently controllable driving, braking, and steering actuators provide additional degrees of freedom for reducing electric-machine and tire-loss energy. This paper presents a real-time longitudinal–steering coordinated torque-vectoring framework for a vehicle driven by four in-wheel motors. A model-predictive active-front-steering controller coordinates the front-wheel steering angle and external yaw moment to reduce steering resistance and lateral tire-slip loss. A reduced inter-axle propulsion problem is analyzed using the Karush–Kuhn–Tucker conditions, and its speed-dependent switching threshold is calibrated offline by particle swarm optimization. Regenerative braking is allocated by an Energy-Optimized Distribution curve subject to ideal-distribution and regulatory constraints. For general positive-torque operation, sequential quadratic programming distributes the four wheel torques by considering motor input power, tire-slip energy, total torque, yaw-moment demand, and actuator limits. Hardware-in-the-loop results under the United States high-acceleration driving cycle and a double-lane-change maneuver show that the proposed strategy reduces energy consumption relative to uniform and tire-utilization-based torque-vectoring strategies. Full article

This paper introduces a rigorous class of two-dimensional Markov-switching autoregressive moving-average (2D MS-ARMA) models for spatial lattice data exhibiting regime-dependent dynamics. The switching mechanism is governed by a latent causal Markov random field that drives spatial transitions between regime-specific autoregressive and moving-average structures. We provide sufficient conditions for the existence of a strictly stationary solution through the top Lyapunov exponent associated with a sequence of random matrices obtained from a state-space representation constructed along the lexicographic order. For the first-order bidirectional specification, we derive explicit spectral conditions linking stationarity to the regime-dependent spectral radii. Sufficient conditions ensuring the existence of finite second-order moments are also provided. Parameter estimation is carried out using a variational expectation–maximization (VEM) algorithm based on a mean-field approximation of the posterior distribution of the hidden regimes. The E-step yields closed-form coordinate ascent updates, while the M-step relies on gradient-based numerical optimization with derivatives computed via recursive differentiation. Under increasing-domain asymptotics, we discuss the consistency and asymptotic behavior of the variational estimator. The proposed framework fills a methodological gap between classical one-dimensional Markov-switching ARMA models and spatial autoregressive structures by extending regime-switching theory to multi-indexed processes with rigorous probabilistic foundations. It provides a comprehensive basis for statistical inference, model diagnostics, and prediction in spatially heterogeneous environments. Full article

Battery-balancing circuits are essential for improving the performance, safety, and service life of lithium-ion battery packs in electric vehicles and energy storage systems. This paper proposes a modified multi-port half-bridge DC–DC circuit with a reconfigurable port network and its control method for battery balancing and multi-level DC voltage output. The circuit evolves from traditional inductor-based balancing units, while a new sequential turn-off switching strategy is introduced so that only one switch is turned off at any moment, achieving precise voltage distribution by adjusting the duty cycle. To improve control accuracy, a dual closed-loop voltage-current control strategy with adaptive gain scheduling and nonlinear compensation is employed. Furthermore, a predictive voltage control strategy based on Mamba-Multilayer Perceptron optimized by the Crested Porcupine Optimizer (CPO-Mamba-MLP-PVC) is proposed. This data-driven approach predicts a target voltage that considers battery and circuit losses, thereby optimizing the balancing path. Experimental results obtained from a hardware prototype verify both battery equalization and multi-level DC output functions. Compared with conventional methods, the proposed CPO-Mamba-MLP-PVC strategy reduces the balancing time by 18.03% and increases the energy utilization rate to 90.7%. Full article

Spacecraft pursuit–evasion in contested environments is complicated by strategic incompleteness: the evader can switch maneuvering modes and deploy multi-domain countermeasures that degrade the pursuer’s perception, leading to non-stationary information and distributionally ambiguous interference statistics. A dynamic time-window Nash equilibrium framework is developed for linearized Local Vertical Local Horizontal (LVLH) relative motion under interference-induced uncertainty. Perceptual degradation is modeled via an evidence–theoretic belief representation, and the Jensen–Shannon (JS) divergence is introduced to quantify discrepancies between nominal and interference-corrupted beliefs. The divergence metric drives an adaptive time-window partitioning policy and an uncertainty-aware running cost that balances nominal performance objectives with robustness regularization during high-degradation intervals. In each time window, sufficient conditions are provided for the existence of a local Nash equilibrium, and equilibrium strategies are characterized by the Hamilton–Jacobi–Bellman–Isaacs (HJBI) equation. A global consistency result is established: assuming state continuity, additive cost decomposition, and dynamic-programming compatibility at window boundaries, concatenating the window-wise equilibria yields a Nash equilibrium over the entire horizon. Unlike conventional receding-horizon differential games with a fixed replanning grid, the proposed policy partitions the horizon online in response to perceptual-degradation events and stitches adjacent windows through a continuation value. This boundary stitching enables the global consistency guarantee under additive costs and state continuity. To hedge against ambiguity in interference intensity, a variational distributionally robust optimization (DRO) problem with moment-constrained ambiguity sets is formulated, and the dual worst-case distribution is derived. The resulting Karush–Kuhn–Tucker (KKT) system is reformulated as a finite-dimensional variational inequality, for which an accelerated Alternating Direction Method of Multipliers (ADMM) operator-splitting solver is proposed for efficient real-time computation. Numerical simulations validate the framework and demonstrate improved robustness and computational scalability under time-varying interference compared with fixed-window baselines. Full article

This paper presents the design of a dual-frequency energy–frequency composite selective surface based on a double-period nested cross-fractal structure. The unit cell consists of a composite metallic layer loaded with diodes, an F4B dielectric substrate, and an intermediate layer with cross-shaped feeding line. The proposed model is structurally optimized and characterized using the periodic method of moments theory and the equivalent circuit method. In addition, its performance was verified through a comparative study. The results demonstrate that under low-power conditions, the surface achieves stable frequency-selective transmission at 2.4 GHz (S-band) and 4.2 GHz (C-band), enabling highly efficient signal transmission with an insertion loss of less than 0.6 dB. Under a high field strength, it automatically switches to an energy-selective state, providing full-band shielding effectiveness of ≥18 dB across a 2.0–5.0 GHz broadband, thereby achieving stealth functionality. The designed composite selective surface exhibits excellent angular stability and features a simple biasing network that does not require additional feeding lines. Thus, this study presents a new approach for designing such surfaces for operation in the microwave regime. Full article

Distributed drive electric vehicles (DDEVs) offer remarkable advantages in handling stability owing to the independent torque and steering control of each wheel. Traditional in-dependent strategies have the disadvantages of slow response speed and unsmooth control interval switching. To overcome the performance tradeoffs of traditional independent strategies, this study proposes an integrated control approach combining four-wheel steering (4WS) and direct yaw moment control (DYC) to achieve coordinated multiobjective optimization. Based on phase-plane theory, the vehicle’s stable domain is divided using a double line method, and speed-dependent control regions and weights are designed to enable smooth switching between control modes. Simulation results demonstrate that, in high-adhesion conditions, compared with the DYC-only strategy, the integrated system reduces the maximum sideslip angle by about 77.8% and the cost function peak by 22.4%. Moreover, it decreases the maximum rear-wheel steering angle by 38.4% and maximum sideslip angle by about 15.4% compared with 4WS-only strategy. Under low-adhesion conditions, compared with the DYC-only strategy, the integrated system reduces the maximum sideslip angle by about 21.1% and the cost function peak by 37.6%. Additionally, the integrated system decreases the maximum rear-wheel steering angle by 60.2% and maximum sideslip angle by about 64.3% compared with 4WS-only strategy. Full article

To address the triple challenges of data sparsity, highly nonlinear dynamics, and maneuver uncertainty in tracking non-cooperative targets in cislunar space, we propose a collaborative framework combining Particle Filter (PF) and Unscented Kalman Filter (UKF). This framework optimizes search efficiency through a two-phase strategy: in the search phase, PF constructs the target reachable domain and leverages undetected information to dynamically shrink the search scope; upon target detection, the framework switches to UKF for high-precision and low-overhead tracking. To overcome the computational bottleneck in high-dimensional reachable domain integration, we integrate a non-product-type Designed Quadrature (DQ) method—one that generates minimal quadrature point sets to replace traditional Monte Carlo sampling by matching the moment conditions of mixed distributions via Gauss–Newton optimization. Distinct from existing single-filter or reachability modeling approaches, the key novelties of this work lie in a two-phase PF-UKF switching framework tailored to the unique cislunar environment resolving the trade-off between search capability and computational efficiency and integration of the non-product DQ method to break the dimensionality curse in high-dimensional reachable domain computation ensuring both moment-matching accuracy and real-time performance. This work holds potential to support space domain awareness and cislunar mission safety: reliable tracking of non-cooperative targets is a key prerequisite for avoiding collisions, safeguarding space assets, and enabling effective space defense, and the proposed framework provides a feasible technical path for this goal through simulation validation. Simulations demonstrate that on a three-dimensional Distant Retrograde Orbit (DRO) observation platform, successful recapture of cislunar transfer orbit targets can be achieved. Under fifth-order accuracy conditions, the system exhibits a position error of $3.745\times {10}^{-1}\mathrm{km}$ and a velocity tracking error of $9.703\times {10}^{-3}\mathrm{m}/\mathrm{s}$ for target search-and-tracking tasks, with a system response time of 1.8343 h. Compared with the traditional PF + numerical integration method, our proposed PF-UKF framework achieves an 86.7% reduction in time cost and a 24.1% reduction in position error. Full article

Large-scale production systems (LSPS) operate under growing complexity driven by digital transformation, tighter environmental regulations, and the demand for resilient and resource-efficient operation. Conventional control strategies, particularly PID and isodromic regulators, remain dominant in industrial automation due to their simplicity and robustness; however, their capability to achieve near-optimal performance is limited under constraints on control amplitude, rate, and energy consumption. This study develops an analytical–computational approach for the approximate realization of optimal nonlinear control using standard regulator architectures. The method determines switching moments analytically and incorporates practical feasibility conditions that account for nonlinearities, measurement noise, and actuator limitations. A comprehensive robustness analysis and simulation-based validation were conducted across four representative industrial scenarios—energy, chemical, logistics, and metallurgy. The results show that the proposed control strategy reduces transient duration by up to 20%, decreases overshoot by a factor of three, and lowers transient energy losses by 5–8% compared with baseline configurations, while maintaining bounded-input–bounded-output (BIBO) stability under parameter uncertainty and external disturbances. The framework provides a clear implementation pathway combining analytical tuning with observer-based derivative estimation, ensuring applicability in real industrial environments without requiring complex computational infrastructure. From a broader sustainability perspective, the proposed method contributes to the reliability, energy efficiency, and longevity of industrial systems. By reducing transient energy demand and mechanical wear, it supports sustainable production practices consistent with the following United Nations Sustainable Development Goals—SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production). The presented results confirm both the theoretical soundness and practical feasibility of the approach, while experimental validation on physical setups is identified as a promising direction for future research. Full article

This paper delves into a multi-player non-renewable resource extraction differential game model, where the duration of the game is a random variable with a composite distribution function. We first explore the conditions under which the cooperative solution also constitutes a Nash equilibrium, thereby extending the theoretical framework from a fixed duration to the more complex and realistic setting of random duration. Assuming that players are unaware of the switching moment of the distribution function, we derive optimal estimates in both time-dependent and state-dependent cases. The findings contribute to a deeper understanding of strategic decision-making in resource extraction under uncertainty and have implications for various fields where random durations and cooperative strategies are relevant. Full article

To achieve precise trajectory tracking and lateral-roll stability during the coordinated control of high-speed autonomous vehicles under lane-changing conditions, this paper proposes an integrated control strategy based on state estimation with a high-order sliding mode and a double-power sliding mode. Firstly, establish a three-degrees-of-freedom vehicle dynamics model and trajectory-tracking error model that includes yaw lateral-roll coupling, and use an extended Kalman filter to estimate real-time unmeasurable states such as the center of mass roll angle, roll angle, and angular velocity. Then, for the trajectory-tracking subsystem, a high-order sliding-mode controller is designed. By introducing a virtual control variable and an arbitrary-order robust differentiator, the switching signal is implicitly integrated into the derivative of the control variable, significantly reducing chattering and ensuring finite-time convergence. Furthermore, in the lateral stability loop, a double-power convergence law sliding-mode controller is constructed to dynamically allocate yaw moment and roll moment with estimated state as feedback, achieving the decoupling optimization of stability and tracking performance. The joint simulation results show that the proposed strategy significantly outperforms traditional sliding-mode schemes in terms of lateral deviation, heading deviation, and key state oscillations under typical high-speed lane-changing conditions. This can provide theoretical basis and engineering reference for integrated control of autonomous vehicles under high dynamic limit conditions. Full article

This paper addresses the problem of optimal stabilization for stochastic dynamical systems characterized by Markov switches and concentration points of jumps, which is a scenario not adequately covered by classical stability conditions. Unlike traditional approaches requiring a strictly positive minimal interval between jumps, we allow jump moments to accumulate at a finite point. Utilizing Lyapunov function methods, we derive sufficient conditions for exponential stability in the mean square and asymptotic stability in probability. We provide explicit constructions of Lyapunov functions adapted to scenarios with jump concentration points and develop conditions under which these functions ensure system stability. For linear stochastic differential equations, the stabilization problem is further simplified to solving a system of Riccati-type matrix equations. This work provides essential theoretical foundations and practical methodologies for stabilizing complex stochastic systems that feature concentration points, expanding the applicability of optimal control theory. Full article

This study, in response to the optimization needs of fall risks for the elderly in the context of cultural and tourism integration in Hebei Province, China, established a quantitative correlation system between ten gait parameters and ten types of spatial risk factors. By collecting gait data (Qualisys infrared motion capture system, sampling rate 200 Hz) and spatial parameters from 30 elderly subjects (with mild, moderate, and severe functional impairments), a multi-level regression model was established. This study revealed that step frequency, step width, and step length were nonlinearly associated with corridor length, door opening width, and step depth (R² = 0.53–0.68). Step speed, ankle dorsiflexion, and foot pressure were key predictive factors (OR = 0.04–8.58, p < 0.001), driving the optimization of core spatial factors such as threshold height, handrail density, and friction coefficient. Step length, cycle, knee angle, and lumbar moment, respectively, affected bed height (45–60 cm), switch height (1.2–1.4 m), stair riser height (≤35 mm), and sink height adjustment range (0.7–0.9 m). The prediction accuracy of the ten optimized values reached 86.7% (95% CI: 82.1–90.3%), with Hosmer–Lemeshow goodness-of-fit x² = 7.32 (p = 0.412) and ROC curve AUC = 0.912. Empirical evidence shows that the graded optimization scheme reduced the fall risk by 42–85%, and the estimated fall incidence rate decreased by 67% after the renovation. The study of the “abnormal gait—spatial threshold—graded optimization” quantitative residential layout optimization provides a systematic solution for the data-quantified model of elderly-friendly residential renovations. Full article

Solving the common paradoxical problem between sub-micro-arc level resolution and a wide range of rotation angles in rotary actuators, this paper designs a single-drive compliant rotary mechanism (CRM) and develops a cross-scale compliant rotary actuator (CCRA). Specially, the proposed CRM employs a single-input–four-output divergent parallel configuration to transform a unidirectional input force into a rotational moment around the rotational center, effectively avoiding asynchronous motion and rotational center shift caused by the multiple actuation. Moreover, the CCRA is developed based on the CRM and a direct-drive rotary (DDR) motor, and adaptive switching between the macro- and micro-combination can simultaneously achieve large rotary range and sub-µrad resolution. After a series of modeling, mechanism optimization, and simulation, a prototype experimental system was built to further test the performance of proposed CCRA. The open-loop and closed-loop characterization experiments showed that the CRM can achieve a rotational resolution of 0.05 $\mathsf{\mu }$rad and a driving force of 0.78 N·m. In addition, the cross-scale switching experimental results show that the CCRA is able to achieve a static positioning accuracy of 3.5 $\mathsf{\mu }$rad within a $\pm 5^{\circ }$ rotational range. Full article