Search Results (5,563)

This paper presents a design methodology for the automatic control system of an airship, accounting for atmospheric disturbances and parametric modeling uncertainties. The derived design model is a linear stochastic system with multiplicative noise incorporating both the airship’s uncertain dynamics and the wind model. First, an $H_{\infty }$ state feedback control law is derived for the stochastic system to ensure robust stability and trajectory tracking performance. Subsequently, a robust Kalman filter is designed to estimate the turbulence model states based on available measurements. It is demonstrated that the optimal gain of this robust filter depends on the solution to a coupled system of specific Riccati and Lyapunov equations. Numerical results indicate improved heading-tracking performance for the considered turbulent simulation when the estimated wind-gust states are incorporated into the feedback law. Under parametric uncertainty, the robust Kalman-type filter also exhibits lower sensitivity of the heading-estimation error than the classical Kalman filter. Full article

Current limiting turns the closed loop of a grid-forming converter into a mode-switching system. For projection-based (feedback-optimization) limiting, this paper shows that conventional linearization fails at the constraint boundary in two distinct ways, and that dynamic mode decomposition (DMD) of trajectories of a nonlinear averaged-dq model recovers the physically consistent spectra. At unconstrained operating points, the Jacobian of the smooth primal–dual field is evaluated where that field is not stationary, and it contains a spurious lightly damped mode belonging to neither the projected nor the unprojected formulation. At constrained operating points, the dual flow pins the constraint whenever the multiplier is active and, because the current predictor is exact in steady state, a backstop limiter sharing the projection limit places the raw current reference on the limiter kink; the constrained equilibria are then non-isolated and path-dependent, and the two well-conditioned one-sided Jacobians differ by an order-one amount. Audited against branch-consistent and one-sided linearizations and certified by out-of-sample reconstruction, DMD recovers the correct spectra over a two-parameter operating map and flags the non-linearizable points. A matched comparison with saturation and virtual-impedance baselines attributes the loss of the network resonance to hard clipping of the bridge current, which instead exposes a lightly damped filter resonance; a backstop above the projection limit restores unique, linearizable equilibria at the price of retaining the resonance. Sensitivities to identification settings, grid strength, controller gains, control delay, and measurement noise are quantified, and a three-converter microgrid study shows a sustained oscillation where a shared threshold admits no equilibrium. Full article

Renewable power plants with co-located battery energy storage systems (BESSs) coordinate forecast-deviation control, renewable-surplus management, electricity-price arbitrage, and ancillary-service commitments through the shared power and energy capability of the battery. This study develops a layered framework for scenario-based intraday multi-service co-optimization and grid-value assessment. The plant-level mixed-integer linear programming (MILP) model operates at 15 min resolution and represents piecewise deviation penalties, time-varying export limits, battery dynamics, a terminal state-of-charge condition, throughput-based degradation costs, and the opportunity cost of reserving regulation headroom. Its scenario-contingent formulation evaluates expected intraday operating value across ten representative photovoltaic trajectories under a perfect-information structure. A complementary service map assigns fast dynamic and system-level indicators to the control, power-flow, and dispatch models appropriate to their time scales. The numerical study considers an 80 MW photovoltaic plant with a 10 MW/20 MWh BESS, evaluates four regulation-compensation cases, and records a complete C4 run time of 209.421893 s on an Intel Core i7-10710U computer with 16 GB RAM. Energy-oriented service stacking increases expected daily plant benefit in the case study, while regulation reservation becomes attractive when compensation exceeds the opportunity cost of battery power and energy headroom. The framework provides a consistent basis for interpreting service interactions, decision timing, data provenance, and plant- and system-level value. Full article

Automated fibre placement (AFP) is a key process for manufacturing advanced polymer–matrix composite aerostructures; however, an uneven compaction-pressure distribution of tows during lay-up reduces the inter-laminar bonding strength and induces processing defects such as bridging and wrinkles. In this study, a pressure regulation strategy coordinating simulation-based feed-forward planning with closed-loop feedback control is proposed as a modified processing route for improving the manufacturing quality of polymer composites. The strategy adopts a two-layer “planning–execution” architecture. In the planning layer, process parameters and placement trajectories are optimized in advance based on a compaction-roller pressure simulation model and a compaction-uniformity index. In the execution layer, closed-loop pressure control suppresses on-site disturbances in real time to ensure that the simulation objectives are realized, thereby achieving uniform control of the compaction-pressure field. Finally, multi-angle lay-up experiments were carried out on a convex-surface mould, and the pressure field was measured using pressure-sensitive films. The results show that the proposed regulation strategy improves pressure distribution uniformity by more than 6%, effectively enhancing curved-surface lay-up quality and the consolidation quality of advanced polymer composite components. This work provides an intelligent design-and-manufacturing route that links process simulation with real-time control for high-quality automated composite processing. Full article

The development of complex equipment faces prominent challenges, including unequal status among participating agents, multi-objective full confrontation, strong super-conflict among multi-indicators, dynamic risk evolution, delayed on-site perception, and the absence of collaborative negotiation. Traditional risk management and control methods, based on the ideal assumptions of equal subjects and independent indicators, struggle to characterize and resolve super-conflict games dominated by super decision-makers. Furthermore, they lack dynamic early warning and closed-loop execution mechanisms linked to real-time perception, commonly suffering from drawbacks such as low early warning accuracy, high decision-making conflict, delayed response, and inefficient collaboration. To address these issues, this paper integrates multi-agent conflict negotiation with intelligent perception and learning technologies to propose an intelligent risk early warning and closed-loop control method for complex equipment development. The three-dimensional risk evolution dynamics model and Virtual Risk Center (VRC) are employed to decouple super-conflict indicators, while the industrial inspection unmanned aerial vehicle (UAV) perception relative motion model enables the unified mapping of physical risks and decision-making games. A super-conflict gray target negotiation (SCGTN) model is constructed to achieve stable consensus decisions among multiple parties under conflicting indicators. Based on Markov Decision Processes and the PPO algorithm, the optimal virtual risk trajectory is generated, which is then combined with the Archimedean spiral convergence trajectory to synthesize executable control trajectories. This forms an integrated system of UAV real-time perception → super-conflict resolution → intelligent decision-making → closed-loop regulation. Validated through a case study of large-scale complex aviation equipment development, the proposed method achieves field-validated risk early warning accuracy of 94.7% evaluated against real-world on-site ground truth labels. The numerical simulation results, whose parameters are fully calibrated against real-world engineering datasets, indicate that under simulated test conditions, our method yields simulation-predicted performance: it reduces decision-making conflict intensity by 49.3%, controls risk deviation error within 1.38%, and shortens closed-loop response time to 158 ms. Note that conflict reduction level, risk deviation error, and closed-loop response time are pure simulation outputs and have not been directly measured from physical on-site closed-loop experiments. Under the same simulation setup, the end-to-end response speed is 7.6 times faster than the peer dynamic closed-loop Digital Twin-Proximal Policy Optimization (DT-PPO) benchmark algorithm with identical online sensing and reinforcement learning architecture and roughly 4700 times faster than simulated counterparts of traditional static offline evaluation modes that rely on periodic manual statistics and offline meetings. It is adaptable to complex equipment development scenarios characterized by strong super-conflict, high dynamics, and unequal subjects, providing a theoretical framework and technical support for intelligent risk prevention and control throughout the full lifecycle of complex equipment. Full article

Free-floating space manipulators are strongly coupled systems in which manipulator motion affects spacecraft base motion through momentum exchange, making simultaneous end-effector control and disturbance suppression challenging. This work investigates how an additional actuated prismatic degree of freedom influences whole-arm coordination in a 6R1P free-floating space manipulator. Compared with a fixed-length 6R configuration, the prismatic joint enlarges the feasible motion space and introduces an additional motion-allocation direction for full-pose tasks under generalized-Jacobian constraints. A proximal policy optimization (PPO)-based controller is developed for full-pose reaching with spacecraft-motion-aware objectives. Simulation results show that the 6R1P configuration improves reaching performance and reduces spacecraft reaction compared with the locked-prismatic 6R baseline. Trajectory-level dynamic reconstruction further reveals that the disturbance reduction is not caused by direct cancellation from the prismatic joint itself, but mainly by configuration-dependent redistribution of revolute-joint motions and enhanced mutual cancellation among their reaction contributions. These results demonstrate that telescopic redundancy provides a mechanism for coordinated motion allocation in free-floating manipulation, enabling learned policies to exploit additional degrees of freedom for improved task execution and reduced spacecraft disturbance. Full article

Shale oil reservoirs exhibit strong heterogeneity and complex rock mechanical properties. Accurate geomechanical modeling is crucial for optimizing horizontal well trajectories and designing fracturing parameters. Traditional mechanical experiments are costly and provide limited data, while empirical formulas suffer from poor regional applicability. Machine learning models designed to predict reservoir mechanical parameters commonly overlook lithofacies-specific variations. To overcome this limitation, this paper presents and adopts a novel workflow that combines micromechanical characterization, machine learning prediction, and mechanical upscaling. Nanoindentation tests are first performed in Well W1 in the Ordos Basin to obtain the mechanical properties of different micro-constituents in shale. Then, a machine learning model is used to establish the relationship between conventional well logs and micro-constituent contents, thereby predicting the distribution of micro-constituents along the entire wellbore. Finally, based on micromechanical derivation, the Mori–Tanaka model is applied for mechanical upscaling to obtain the Young’s modulus. The feasibility of this method is validated by comparing the results with those calculated from conventional well log data. This approach combines the high-precision micromechanical characterization of nanoindentation with the predictive capability of machine learning. It overcomes the limitations of elemental capture spectroscopy logging (namely, its high cost and insufficient organic matter information) and provides a new pathway for establishing one-dimensional geomechanical models. Full article

To clarify the controlling effects of the well deviation angle and azimuth angle on the breakdown pressure, propagation morphology, and fracture-network complexity of hydraulic fractures in tight sandstone, true triaxial hydraulic fracturing physical simulations were systematically conducted on Chang 7 Member sandstone from Yanchuan County, Ordos Basin, under different well deviation and azimuth angles. By combining injection-pressure monitoring, surface fracture-morphology observation, and three-dimensional laser scanning, the breakdown pressure, propagation path, surface roughness, fractal dimension, and overall complexity of the fractures were quantitatively analyzed. The results show that, at an azimuth angle of 90°, the breakdown pressure of the sandstone generally decreases as the well deviation angle increases from 0° to 90°, dropping from 19.125 MPa to 13.569 MPa, indicating that horizontal wells are easier to fracture. At a well deviation of 60°, the fracture is more prone to deflect and communicate with natural weak planes, yielding the highest overall complexity (f = 1.629). For horizontal wells under normal-faulting stress, the breakdown pressure decreases as the azimuth angle increases; the lowest breakdown pressure (12.933 MPa) is obtained when the wellbore is drilled along the maximum horizontal principal stress (σ_H), and the highest (18.310 MPa) when parallel to the minimum horizontal principal stress (σ_h). When the azimuth angle is 30°, both the fracture-surface roughness (S_a = 2.037 mm, S_q = 2.691 mm) and the overall complexity (f = 1.679) reach their maxima, which is most favorable for forming tortuous, rough, and complex fracture networks. The fractal dimension of the fractures varies little across the tested conditions (2.0339–2.1379), indicating that it is mainly controlled by the intrinsic heterogeneity of the rock. The research results can provide an experimental basis for the optimization of horizontal-well trajectories and fracturing-parameter design in tight sandstone reservoirs. Full article

In the highly dynamic production environment of intelligent logistics centers, autonomous mobile robots (AMRs) serve as the core carriers connecting automated storage systems with assembly lines. Their operational efficiency directly determines the overall production cycle of the entire facility. In actual working conditions, narrow aisles in line-side material stacking zones often lead to local deadlocks. Additionally, the presence of mixed human–machine traffic and numerous dynamic obstacles causes traditional algorithms to struggle with obstacle avoidance or path oscillation during long-distance delivery. This paper investigates the M150 warehouse mobile robot and proposes an improved Dynamic Window Approach (DWA) integrated with adaptive fuzzy control for local path planning. First, the velocity evaluation function is modified for confined spaces by incorporating angular velocity to enhance escape capability and adding a target distance function to optimize sub-goal tracking. Second, a dynamic sampling space based on obstacle distance is constructed to balance computational load. Finally, a two-input, four-output fuzzy controller is designed to adaptively adjust evaluation function weighting factors in real time, enhancing the AMR’s adaptability across diverse scenarios. MATLAB-based simulations demonstrate that compared to traditional algorithms, the improved DWA produces smoother trajectories, effectively resolving jamming and obstacle avoidance issues, thereby meeting enterprises’ stringent requirements for AMR operational stability and safety. Full article

Polymeric composites reinforced with short or continuous carbon fibers offer exceptional mechanical and tribological properties that outperform their unfilled counterparts. The combination of these excellent properties with the possibility to manufacture custom-made composites with complex shapes and geometry is unique; therefore, additive manufacturing has recently emerged as an intriguing manufacturing technique combining these two features. Specifically, Fused Filament Fabrication (FFF) has attracted significant academic and industrial attention owing to its operational simplicity, cost-effectiveness, and compatibility with a wide range of thermoplastic matrices. This review provides a comprehensive overview of FFF-printed carbon-fiber reinforced composites, focusing on strategies to optimize both their mechanical and tribological properties. The first part introduces the core aspects of FFF technology and highlights how the printing process is altered by the incorporation of carbon fibers. The central part systematically points out the benefits of utilizing short or continuous carbon fibers on the mechanical and tribological performance of the composites. The final section addresses advanced post-processing techniques designed to mitigate typical printing issues, followed by an analysis of current research trajectories. Overall, this review examines the current literature to provide a state-of-the-art summary, highlighting the manufacturing possibilities, inherent limitations, and new research frontiers of FFF-printed carbon fiber-reinforced polymer composites. Full article

Remotely operated vehicles (ROVs) suffer from severe navigation trajectory optimization problems in variable water-depth environments, as near-wall hydrodynamic effects cause synchronous scaling drift of added mass and damping coefficients. This parameter variation leads to obvious model mismatch in traditional fixed-gain controllers and seriously deteriorates ROV trajectory tracking accuracy. To address the scale-type parameter mismatch issue, this paper proposes an adaptive trajectory tracking control strategy combining forgetting-factor recursive least squares (RLS) online identification and periodic linear quadratic regulator (LQR) gain scheduling. A closed-loop coupling framework is established to estimate the discrete state-space matrices of ROVs via the RLS algorithm, and the optimal feedback gains are updated every 50 sampling steps to adapt to time-varying hydrodynamic characteristics. Three typical water-depth scenarios with different parameter mismatch degrees are set up for sinusoidal trajectory tracking simulations, adopting PID and fixed-parameter MPC as comparison methods. The results indicate that the proposed method maintains comparable steady-state performance with fixed-parameter MPC under nominal conditions, and reduces the two-dimensional trajectory RMSE by 8.4% and 57.4% under moderate and severe parameter mismatch conditions, respectively. A critical mismatch threshold of fixed-parameter MPC compensation capability is also determined. This study provides a feasible technical reference for high-precision adaptive motion control of ROVs in variable-depth water environments. Full article

Maize (Zea mays L.) plays an irreplaceable role in food, feed, and energy security. However, abiotic stresses, including drought, high temperature, salinity, and waterlogging, driven by climate warming, cause yield losses of 20–30% of global maize production every year, posing a serious threat to global food security and human development. Traditional approaches such as genetic improvement, agronomic optimization, and chemical regulation struggle to address the multiple stresses affecting maize. Alginate oligosaccharides (AOSs)—linear oligosaccharides with degrees of polymerization of 2–20—can systematically activate plant immunity, reshape root architecture, enhance antioxidant defense, and regulate ion homeostasis to simultaneously achieve yield enhancement and stress tolerance. This review assesses global maize production and abiotic stress-induced losses and analyzes the limitations of existing yield stabilization technologies. The molecular physiological networks through which AOSs regulate maize yield formation and stress adaptation are also elucidated. Furthermore, through bibliometric analysis, we reveal the evolutionary trajectory from phenotypic observation to mechanistic dissection and identify current knowledge gaps. Finally, we propose future research priorities, including AOS receptor identification, model applications of AOSs, and combined-stress regulation by AOSs. This review provides theoretical foundations and practical pathways for incorporating AOSs into sustainable maize development. Full article

Large-scale green hydrogen production via PEM electrolysis demands control strategies that surpass the limitations of traditional distributed control systems (DCSs). Digital twin (DT) technology has been introduced as a structured design framework for predictive maintenance and operational optimization across hydrogen production pathways, from steam methane reforming and green ammonia to next-generation PEM electrolyzer. A critical constraint exists; passive DT architectures cannot autonomously close the control loop. The resulting prediction–action latency gap introduces delays of 28–120 min precisely when dynamic renewable energy loads require sub-second responses. This review makes three original contributions; it characterizes the prediction–action latency gap as a structural design constraint across SMR, green ammonia, and PEM electrolyzer DT deployments, based on a structured Scopus and Web of Science search; it proposes a three-tier DCS–digital twin–AI architecture as the solution; and it defines five purpose-designed AI algorithm modules—Stack State Estimator, Degradation Trajectory Predictor, Fleet Dispatcher, Anomaly and Fault Classifier, and Maintenance Scheduler—together with an eight-challenge research agenda with technology readiness level assessments. All projections are extrapolated from adjacent domains and require electrolyzer-specific experimental validation. Full article

Background: Conventional zygomatic implant placement may be technically impossible in patients with severe trismus or extensive post-maxillectomy defects because adequate intraoral access is required. This study evaluated the feasibility, accuracy, anatomical safety, and prosthetic correspondence of a novel reverse zygomatic implant inserted from the zygomatic surface toward the oral cavity. Materials and Methods: Nine fresh-frozen cadaveric heads underwent simulated Brown Class II maxillectomies. Thirty-six reverse zygomatic implants were virtually planned at positions 13, 15, 23, and 25 and placed using specimen-specific CAD/CAM drilling guides. A superior blepharoplasty approach and an inferior transconjunctival approach with lateral canthotomy and cantholysis were evaluated. Postoperative CT superimposition was used to measure linear and angular deviations. Zygomatic bone volume, implant trajectory, primary stability, complications, and correspondence with a specimen-specific polyamide verification bar were also assessed. Results: Thirty-five of the 36 implants achieved primary stability, corresponding to a technical success rate of 97.2%. Mean deviation was 2.22 ± 1.41 mm at the zygomatic entry point and 4.41 ± 1.82 mm at the intraoral emergence point. Mean angular deviation was 3.83 ± 1.71°. No statistically significant differences were identified according to implant position, laterality, surgical access route or zygomatic bone volume. Two anterior zygomatic cortical fractures occurred; one resulted in loss of primary stability. No orbital or infratemporal penetration was observed. Complete prosthetic correspondence was obtained at a median of two of the four abutments per specimen, and none of the verification bars achieved complete passive seating over all four abutments. Conclusions: Guided reverse zygomatic implant placement was technically feasible and reproducible regarding implant stability in this cadaveric model. However, cortical fracture risk and incomplete prosthetic correspondence indicate that further optimization and prospective clinical validation are required before routine clinical application. Full article

Unmanned aerial vehicle (UAV)-assisted data collection from battery-constrained Internet of Things (IoT) devices faces a fundamental trade-off between communication reliability and device energy depletion. This paper jointly optimizes UAV trajectory, user association, and radio resource management (RRM) to minimize the uplink weighted sum transmit powers, where each device’s weight is dynamically and inversely related to its residual energy, while treating UAV propulsion energy as a feasibility constraint. To circumvent the initialization trap of conventional alternating optimization (AO), we propose a two-layer non-alternating framework. The inner layer solves the per-slot RRM problem analytically via KKT conditions for a fixed UAV position, yielding analytical power allocation and a unique bandwidth solution, while user association is determined by an incremental greedy algorithm. The outer layer formulates trajectory planning (TP) as a Markov decision process (MDP), enabling single-pass trajectory synthesis without cross-layer iteration, thereby inherently avoiding initialization sensitivity. The framework supports the genetic algorithm (GA) and limited depth-first search (DFS) as trajectory solvers, with the deep Q-network (DQN) as a promising future extension, each offering distinct optimality–complexity trade-offs. Simulation results show that the proposed scheme consistently outperforms conventional iterative baselines across various network configurations. Full article