This paper investigates the problem of accurate and fast trajectory and attitude tracking for a quadrotor unmanned aerial vehicle (UAV) against unknown external disturbances. To address this issue, a finite-time extended state observer-based model predictive control framework is proposed to enable hierarchical robust tracking performance. Specifically, a finite-time extended state observer (FTESO) with a simplified finite-time convergence term is developed to estimate the lumped disturbances, reducing the structural complexity of the observer while retaining fast finite-time convergence. Next, the real-time disturbance estimates are incorporated into the prediction model to compensate for the mismatch between the nominal model and the actual quadrotor dynamics. On this basis, a Lyapunov-based model predictive control (MPC) scheme with a contraction constraint is constructed to guarantee the closed-loop stability of the position subsystem. Furthermore, a nonsingular fast terminal sliding mode control (NFTSMC) strategy is designed for the attitude subsystem to achieve finite-time convergence and improved tracking accuracy. Theoretical analysis establishes the stability of the overall closed-loop system. Finally, numerical simulations are conducted under representative disturbance scenarios. By incorporating FTESO, the proposed framework achieves a 14.43% reduction in the position-tracking root mean square error (RMSE), demonstrating the effectiveness of online disturbance compensation in improving tracking performance.
Aircraft composite panels are sensitive to positioner layouts during flexible positioning assembly, and inappropriate layouts may cause non-uniform deformation and local stress concentration. To reduce these effects, this study proposes a positioning-process optimization method based on a simulated component that approximately represents the positioning process of a real panel. First, a parametric positioner model is established using CATIA Automation, and sample data are obtained through batch finite element analyses. Gaussian process regression is then used to construct a surrogate model between the positioner coordinates and the combined objective function, and particle swarm optimization is used to search for an optimized positioner layout. Finally, the optimized layout is reintroduced into the finite element model for verification. The results show that the combined process index, consisting of deformation and stress terms, is reduced by 10.389%-28.896% compared with the pre-optimization cases.
This research is motivated by the functional demands of mode selector valves in variable cycle engines, leading to the design of an innovative eight-sector, over-constrained regulation mechanism. It employs eight curved-plate links arranged symmetrically to convert rotational inputs into smooth reciprocating translational motion of a movable octagonal ring. This kinematic configuration enables the precise and repeatable positioning essential for reliable mode selector valve operation. A comprehensive dynamic analysis of this multi-rigid-body system is then undertaken, serving a dual purpose. First, it establishes the theoretical foundation for accurately determining the external loads experienced by the mechanism under various operating conditions. Second, it supports the development of a control algorithm capable of computing the driving forces required to achieve the desired open/close motion. Together, these capabilities form the basis for reliable and efficient actuation of the mode selector valves within a variable cycle engine environment. Building upon a prior kinematic analysis of valve operation, this study investigates the dynamics of the over-constrained system. The dynamic model is formulated using momentum and force screws, a screw mechanics approach that preserves all the strengths of the Newton-Euler method while offering a key computational advantage. It eliminates the need to derive and solve second-order differential equations of motion from positional data. As a result, the implementation code is remarkably concise and straightforward. This efficient methodology provides a simple yet powerful tool for multi-rigid-body system dynamics. Its clarity and computational economy position it for broad adoption in future simulation software dedicated to complex mechanical systems.
This comprehensive review analyzes 53 successful multi-nanosatellite missions between 2016 and 2024, all operational over 6 months from established databases, representing 775 satellites enumerated individually in Table 1, across commercial Earth-observation fleets (Planet Labs Flock/SuperDove, 10 batches; Spire Global Lemur-2, 12 batches), IoT constellations (Swarm Technologies SpaceBEE, 10 batches), and smaller scientific, technology-demonstration and educational clusters. Rapid advances in CubeSat technologies have significantly expanded the range of feasible multi-satellite mission architectures. Missions are classified into three architectures: Strictly Controlled Formation ( n = 2, 4%), Constellation Formation ( n = 35, 66%), and Swarm/Cluster Formation ( n = 16, 30%). By primary application the corpus comprises Earth Observation ( n = 28), IoT/Telecommunications ( n = 12), Technology Demonstration ( n = 6), Scientific Research ( n = 5) and Geodesy & Education ( n = 2). All shares reported in this review are mission-weighted unless a satellite-weighted basis is stated explicitly. Technological enablers critically assessed include GPS relative navigation achieving <1 m accuracy (CanX-4/5 heritage), NanoFEEP low-thrust propulsion for formation maintenance and 25-year deorbit compliance when incorporated into an appropriate end-of-life disposal strategy consistent with international debris mitigation guidelines, and swarm intelligence enabling autonomous task allocation. The end-to-end operational lifecycle from PSLV-C37 record-setting 88-satellite Flock-3p deployment through 200 Mbps SuperDove imaging pipelines to Hybrid Adaptive Reconfigurable Fault Tolerance (HARFT) radiation-resilient architectures is systematically mapped. Operational challenges spanning power constraints, collision avoidance, and scalability are systematically paired with validated mitigations across mission scales. A novel decision framework is proposed to guide architecture selection by matching science objectives to GNC complexity levels. Synthesizing operational insights from record-setting commercial constellations and precision scientific formations, this review establishes the complete formation flying design-to-operation methodology, projecting pathways toward hybrid autonomous networks, robotic servicing, and sustainable LEO infrastructure.
The reliability of uncrewed aerial vehicle (UAV) systems is essential for mission success in safety-critical scenarios such as military surveillance, rescue operations, and disaster assessment. System availability depends not only on subsystem reliability but also on maintenance work to keep UAV systems functioning. This study evaluates UAV reliability characteristics, focusing on availability, reliability, and maintenance man-hours (MMH) under scenarios. Unlike existing UAV reliability studies that primarily focus on overall system reliability and availability, the proposed framework provides comprehensive subsystem-level analysis by identifying critical subsystems, evaluating their impact on system performance, and integrating MMH with preventive replacement planning. A generalized stochastic Petri nets (GSPN) modeling framework is applied to examine failure and repair behavior and assess system availability and reliability. The GSPN determines maintenance personnel and identifies maintenance priorities for subsystems. The MMH is determined using an age replacement strategy to identify optimal replacement timing based on component degradation. The results show that the proposed GSPN-based framework improves system availability from 0.9388 to 0.9905 and enhances system reliability from 0.6157 to 0.9605. In addition, MMH is evaluated under preventive replacement policies corresponding to 60%-90% of characteristic life, enabling replacement intervals for efficient maintenance planning. The framework supports UAV fleet operators, maintenance planners, and defense organizations through comprehensive reliability and availability analysis of the overall UAV system and its critical subsystems. By estimating maintenance manpower, identifying critical subsystems, and determining preventive replacement schedules, the framework supports efficient maintenance planning, improves operational readiness, and minimizes unnecessary maintenance effort and costs.
