Search Results (176)

In this paper, a radial basis function (RBF) neural network based trajectory generation strategy is proposed to solve the online rapid generation of initial reference trajectory for low-cost hypersonic glide vehicles (HGV) under initial state perturbation. Firstly, the feasible trajectories that constitute the sample sets are offline generated by pseudospectral method according to the possible distribution of heights and velocities. Then, the sample set is randomly divided into training subset and test subset, by which the RBF neural network is trained and verified. Moreover, the input of the RBF neural network is a vector comprised by height and velocity from the initial state, whereas the output is a discrete state-control sequence which represents the trajectory from the current state to the expected final state. The simulation results validate that the proposed method has high confidence and small errors, which can improve the on-line generation efficiency of the trajectory. Full article

With the advancement of wide-area measurement systems (WAMSs), response-driven methods for transient stability analysis have gained increasing attention in recent years. The maximum Lyapunov exponent (MLE)-based trajectory analysis technique enables online transient stability assessment by capturing the trend characteristics of system trajectories. Motivated by this capability, a rapid construction methodology for the practical dynamic security region (PDSR) is proposed based on the MLE criterion. Initially, through analyzing the dynamic characteristics of generator rotor angle trajectories after disturbances, the dynamic MLE characteristics of the generator’s angular velocity deviation trajectory are extracted to formulate the MLE-based stability criterion. Subsequently, a stability boundary function based on MLE trajectories is developed, and the linear relationship between the injection space parameters and the MLE stability boundary function is derived. Finally, leveraging the sensitivity of the stability boundary function to the variations in injection space parameters, the dynamic security region is constructed around the dominant instability critical point, thereby establishing a mapping function between transient stability and the injection space parameters. The effectiveness of the proposed method is demonstrated through simulations on the IEEE39 power system. Results show that the method exhibits promising performance in terms of speed and adaptability for transient stability analysis and boundary construction. Full article

Integrated die-cast components reduce machining/assembly steps and improve mechanical dynamic characteristics, eliminating joint loosening/fracture risks after long-term use. However, the highly variable geometries and random spatial distributions of burrs, flash, parting lines, and risers in castings invalidate pre-programmed or teach-in robotic grinding methods. This paper reviews recent progress and future trends in robotic grinding, analyzing four core aspects: force control stability/adaptability (e.g., adaptive impedance control can reduce average force-tracking error to 0.38 N), trajectory planning/path generation (e.g., error-driven compensation can lower contour error by 34.2–55.1%), process parameter optimization, and challenges of sensing latency/quality evaluation (e.g., deep learning models achieve 97.64% accuracy in identifying abrasive belt wear states). The key enabling technologies are summarized, including active/passive compliant force control, model-/data-driven adaptive trajectory planning, intelligent process parameter optimization integrating physical mechanisms and data-driven approaches, and multi-modal state monitoring with online quality assessment. Representative applications (metal castings, aero-engine blades, thin-walled components, weld seams) are presented, and prospective research directions are proposed. This paper provides a comprehensive reference for theoretical research and engineering practice in this field. Full article

Prediction of the reachable domain for high-threat unmanned aerial vehicles (UAVs) is critical for enabling cross-domain flight vehicles to perform proactive avoidance maneuvers. To address this challenge, this paper proposes a novel generic framework that integrates a Radau pseudospectral method (RPM) with a BP neural network, supported by information acquired from satellites. The framework begins by estimating a preliminary state vector of the non-cooperative target, including its coarse position and velocity, via a Newton iterative algorithm. To refine this initial estimate and enable continuous tracking, an Extended Kalman Filter (EKF) is fused with a flight vehicle dynamics model. Subsequently, the RPM is employed to solve the trajectory planning problem, generating a comprehensive database for offline training. This database is then used to train a multilayer feedforward neural network within an offline training and online application framework, which drastically reduces computational complexity and time. Finally, numerical simulations demonstrate the method’s high prediction accuracy and strong robustness against tracking uncertainties. Crucially, the neural network predicts the reachable domain in just 0.01 s, making it highly viable for real-time online applications. Full article

Recent systems for position and movement monitoring are increasingly enhanced by the integration of advanced AI techniques. Besides traditional analytical and algorithmic approaches, which are still applied in areas such as signal processing, sensor fusion, and kinematic modeling, there is a growing body of research that leverages AI-based methods to improve accuracy, robustness, and real-time decision-making capabilities. Artificial neural networks and deep learning methods are more and more often used for tasks such as predicting movement trajectories, detecting position anomalies, and approximating complex motion patterns. The main aim of this work is to provide the main contributions of the recent publications to the current state of the field. Key trends, challenges, and prospects for their future development are also highlighted. Initial statistical analysis was conducted based on responses to queries formulated for searching engines of leading online databases since 2006. Next, the retrieved articles from the last 6 years were subjected to a more detailed analysis. They were divided into thematic areas, including models for human pose estimation; systems for motion detection and tracking, with special attention to human movement; and, eventually, more specialized applications such as action recognition, autonomous driving, motion analysis, and surveillance. The architectures of the created models, the methods for parameter tuning or training, the input datasets used, and the result evaluation metrics were classified. Finally, some more general conclusions were drawn. Full article

Multi-objective flexible job shop scheduling requires balancing conflicting objectives while supporting real-time decision-making in industrial environments. However, although traditional metaheuristics are effective for global search, their high computational cost limits their applicability in time-sensitive scenarios. To address this issue, this paper proposes dual-pool guided preference-conditioned graph reinforcement learning (DPG-GRL), an encoder–decoder framework for the multi-objective flexible job shop scheduling problem. In DPG-GRL, a graph attention network encoder extracts operation and machine-level representations from a heterogeneous graph, while the decoder is conditioned on a preference vector to generate scheduling solutions with different trade-offs using a single trained policy. To improve sample efficiency and training stability, a dual-pool guidance mechanism is introduced, in which an offline expert pool provides a stable behavioral prior for policy initialization and an online elite pool continuously replays high-quality trajectories to refine the policy. Experimental results show that DPG-GRL outperforms representative multi-objective evolutionary algorithms, including the non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective evolutionary algorithm based on decomposition (MOEA/D), on synthetic instances, with more pronounced advantages in solution quality and inference efficiency as the problem scale grows. In addition, evaluations on public benchmark instances using a model trained only on the small synthetic setting demonstrate rapid Pareto-front approximation, high-quality solution sets, and promising generalization to unseen instances. These results indicate the potential of DPG-GRL for real-time production scheduling and energy-aware manufacturing. Full article

To address the challenges of insufficient safety, poor trajectory executability, and low stability of online optimization in UAV inspection path planning for converter valve halls under conditions of dense obstacles, narrow passages, and complex electromagnetic environments, an electromagnetic risk-aware three-dimensional path planning method based on model predictive path integral (MPPI) is proposed. Considering the typical “valve-tower array–bushing” layout in converter valve halls, a three-dimensional obstacle model consisting of prismatic valve towers and cylindrical bushings is established. A comprehensive cost function is then constructed by jointly incorporating path length, safety clearance, trajectory smoothness, and electromagnetic risk. On this basis, an electromagnetic risk term, a corridor guidance term, and a control increment regularization term are integrated into the MPPI receding-horizon optimization framework to enable online generation of inspection trajectories. Simulation results demonstrate that, compared with A*, Vanilla MPPI, and Corridor MPPI, the proposed method generates smoother and safer trajectories and achieves superior overall performance in terms of total cost, minimum clearance, success rate, and mean tracking error. The proposed method provides an effective solution for intelligent UAV inspection path planning in converter valve halls. Full article

To address trajectory-tracking degradation of unmanned surface vehicles (USVs) in constrained waters caused by model uncertainty, strong environmental disturbances, and actuator limitations, this paper proposes a robust disturbance-observer-based optimization model predictive control method. First, a nonlinear tracking error model is established for a 3-DOF USV by incorporating environmental loads, parametric perturbations, and unmodeled dynamics into the kinematic and dynamic equations. Based on this model, a prediction model suitable for model predictive control is derived through linearization and discretization. Then, to estimate complex unknown disturbances online, a robust disturbance observer integrating a radial basis function neural network (RBFNN) with an adaptive sliding-mode mechanism is developed, enabling real-time approximation and compensation of lumped disturbances in the surge and yaw channels. Furthermore, to overcome actuator saturation caused by the direct superposition of feedforward compensation and feedback control in conventional composite strategies, a dynamic constraint reconstruction mechanism is introduced. By feeding the observer-generated compensation signal back into the MPC optimizer, the feasible control region is updated online so that the total control input satisfies both magnitude and rate constraints of the propulsion system. Theoretical analysis based on Lyapunov theory proves the uniform ultimate boundedness of the observation errors and neural-network weight estimation errors, while input-to-state stability theory is employed to establish closed-loop stability. Comparative simulations under sinusoidal trajectories, time-varying curvature paths, and large-maneuver turning conditions demonstrate that the proposed method significantly improves tracking accuracy, disturbance rejection capability, and control feasibility under severe disturbances and parameter mismatch. Full article

For complex tasks such as search and recovery in uncharted maritime areas, the use of heterogeneous unmanned swarms (UAVs and USVs) is highly promising, yet effective cross-domain cooperative trajectory planning remains a key challenge, often leading to mission delays. This paper proposes a rapid Cooperative Cross-domain Path Planning framework (CCPP) and its associated algorithm for heterogeneous UAV–USV swarms. The framework first establishes a visual-fusion modeling pipeline, converting visual pose estimation, uncertainties, and semantic dynamic obstacles into a planning representation with robust safety margins and time-varying risk fields. A hybrid velocity-path co-optimization algorithm is then designed to simultaneously generate curvature-feasible trajectories and speed profiles under heterogeneous kinematics and explicit temporal constraints. In the end, an adaptive interpretable decision tree acts as a meta-strategy for online replanning and real-time adjustment of modes and weights. To address the critical issue of uneven arrival time distribution, this paper introduces, inspired by economic inequality analysis, a normalized Gini coefficient-based arrival time consistency index to quantify and optimize coordination timing. Comprehensive experiments validate the effectiveness of the proposed approach in enhancing cooperative efficiency and real-time adaptability. Full article

Rapid and robust trajectory planning for the Terminal Area Energy Management (TAEM) phase of horizontal-landing Reusable Launch Vehicles (RLVs) is critical but challenging due to large initial deviations, stringent terminal constraints, and strong model nonlinearities. To address the limitations of existing methods in convergence reliability and computational speed, this paper proposes a novel online trajectory optimization framework based on analytical lateral planning and equivalent dynamic decoupling. First, a cubic Bézier curve is employed to parameterize the lateral ground track, enabling the rapid generation of analytical expressions for the lateral states that strictly satisfy boundary constraints. Leveraging these analytical solutions, the original six-degree-of-freedom dynamics are exactly decoupled and reduced to a lower-dimensional model governing only the longitudinal motion. To further mitigate nonlinearity, the third derivative of height with respect to range is introduced as a virtual control variable, transforming the problem into a smoother form. The resulting equivalent longitudinal optimization problem is then efficiently solved using the Gauss Pseudospectral Method. Numerical simulations demonstrate that the proposed method significantly outperforms traditional approaches in computational efficiency: it generates feasible trajectories satisfying all constraints within 0.26 s (3$\sigma$ value). Furthermore, the method exhibits remarkable insensitivity to initial guesses, achieving stable convergence even with simple linear initialization. This approach provides a robust and real-time capable solution for complex TAEM trajectory optimization problems characterized by high nonlinearity and multiple constraints. Full article

To address the emergency obstacle-avoidance problem of intelligent vehicles on structured roads, this paper proposes an integrated planning and control method that combines an improved Artificial Potential Field (APF) with fuzzy Model Predictive Control (MPC). Different from a direct APF + MPC combination, the planning layer introduces a braking-distance threshold, an effective obstacle-influence boundary, and sinusoidal shape factors to reshape the obstacle repulsive field and alleviate local-minimum behavior. A seventh-order polynomial smoothing strategy is then adopted to generate a reference path with higher-order continuity. For trajectory tracking, a fuzzy adaptive MPC controller adjusts the prediction horizon and control horizon online according to lateral error, while a fuzzy PID controller regulates longitudinal speed. MATLAB/Simulink and CarSim co-simulation results in single-static, double-static, and double-dynamic obstacle scenarios show that the proposed method can generate smoother trajectories and achieve more stable tracking, thereby improving obstacle-avoidance safety and ride comfort. In the double-static scenario, the peak lateral error is reduced from about 0.7 m to within 0.1 m, while in the double-dynamic scenario the longitudinal speed is maintained within 78–80 km/h instead of dropping to about 67 km/h under the baseline controller. The study provides a practical technical framework for integrated decision-planning-control design in structured-road intelligent vehicles. Full article

Cooperative multi-UAV pursuit–evasion under occlusions and sensor noise is challenged by intermittent observability of the evader, varying observation-window lengths, and non-stationary evader tactics, all of which destabilize prediction and undermine safety-constrained cooperation. To address these challenges, we propose a safe decision-making framework that uses behavior mode and subgoal inference as intermediate representations for interpretable, uncertainty-aware cooperation. Specifically, an observation-driven generative intent–subgoal model infers the evader’s behavior mode and subgoal from short observation windows. Building on this model, a length-agnostic trajectory predictor is trained via multi-window knowledge distillation and consistency regularization to produce future trajectory predictions with calibrated uncertainty for arbitrary observation-window lengths, thereby reducing cross-window inference inconsistency and lowering online computational cost. Based on these predictions, we derive belief and risk features and develop a belief–risk-gated hierarchical multi-agent policy based on soft actor-critic with a safety projection layer, enabling adaptive strategy switching and a controllable trade-off between efficiency and safety. Experiments in obstacle-rich pursuit–evasion environments with randomized layouts and diverse obstacle configurations demonstrate more stable cooperative capture, safer maneuvering, and lower decision variance than representative baselines, indicating strong robustness and real-time feasibility. Specifically, across different observation-window settings, the proposed method improves the normalized expected return by approximately 5–7% over the strongest baseline and reduces pursuer losses by roughly 22–25%. Moreover, its end-to-end decision latency consistently remains within the 50 ms control cycle. Full article

This paper focuses on the dynamics modeling and control methods for an underactuated Dual-Unmanned Aerial Helicopter Slung Load System (DUH-SLS), which consists of two Unmanned Aerial Helicopters (UAHs) connected to the suspended load via two sling cables. The DUH-SLS is a multi-body coupled system with internal ideal constraint forces and has seven motion degrees of freedom (DOFs) in the longitudinal plane. In this paper, a set of independent and complete generalized coordinates is selected to describe the system’s motion. The dynamics model of DUH-SLS is established using Lagrange analytical mechanics. This approach, which avoids system internal forces, greatly improves modeling efficiency. Finally, the correctness of this dynamics model is validated using a virtual prototype of the DUH-SLS developed in the multi-body dynamics simulation software ADAMS. The DUH-SLS is a complex nonlinear controlled object, and the iterative Linear Quadratic Regulator (iLQR) method is introduced to design an integrated optimal controller to achieve trajectory tracking and swing suppression for the DUH-SLS. This method transforms the quadratic optimal control problem of nonlinear systems into a series of linear quadratic optimal control (LQR) problems through iterative optimization in function space, thus obtaining an optimal solution. The iLQR optimal controller requires offline iterative computation, but the optimal control obtained has a state feedback closed-loop form, which ensures robustness during online control. Numerical simulation results demonstrate that the proposed iLQR optimal controller exhibits excellent control performance in complex multi-task scenarios. Particularly in trajectory tracking tasks, the maximum average position tracking error of the iLQR controller is only 0.14 m, compared to 3.57 m and 3.11 m for the LQR and LMC (Lyapunov Method Controller) controllers, respectively. Furthermore, the controller demonstrates strong robustness against internal parameter perturbations and external complex wind disturbances, fully validating the effectiveness and superiority of the proposed approach. Full article

In nanoscale wafer defect inspection, raster scan imaging imposes sub-micrometer requirements on motion stage tracking accuracy, while trajectory changes and load variations pose significant challenges to traditional control methods. This paper proposes a Real-time Iterative Compensation based Adaptive Robust Control (RICARC) strategy. Within this framework, the ARC module incorporates RLS-based online parameter estimation, a PID-type feedback control term, and a robust control term to suppress lumped disturbances. On this basis, the RIC module establishes a discrete prediction model based on the ARC closed-loop system and iteratively generates optimal feedforward compensation signals at each sampling instant to further suppress residual tracking errors. Experimental results across five operating scenarios, including periodic, dual-frequency, and S-curve trajectories, as well as payload variation, and strong external disturbances, demonstrate that RICARC consistently achieves sub-micrometer RMS accuracy ranging from 0.120 to 0.240 μm, reducing RMS errors by over 75% compared with conventional ARC, effectively enhancing imaging quality in nanoscale wafer defect detection systems. Full article

Background: Mothers with gestational diabetes mellitus (GDM) face specific challenges in breastfeeding, yet data on the long-term effectiveness of mobile-based interventions remain limited. This study aimed to evaluate the long-term effectiveness of a Mobile-Based Breastfeeding Promotion Program for GDM (M-BFGDM) on breastfeeding knowledge, self-efficacy, and practice rates up to 6 months postpartum. Methods: A nonequivalent control group pretest–posttest quasi-experimental study was conducted. Participants were recruited from an online community. The intervention group received the M-BFGDM, which included Information–Motivation–Behavioral Skills (IMB) model-based educational videos and KakaoTalk counseling. Data were collected at prenatal, 1-week, 1-month, and 6-month postpartum time points. Data were analyzed using generalized estimating equations (GEE) and repeated-measures ANOVA. Results: The final analysis included 38 participants (experimental group, n = 18; control group, n = 20). The M-BFGDM was effective in improving breastfeeding knowledge among women with GDM (p = 0.003). However, the intervention did not significantly influence the trajectory of breastfeeding self-efficacy or prevent the decline in practice rates over 6 months compared to the control group. Conclusions: These findings suggest that while mobile education enhances knowledge, sustained breastfeeding requires more intensive, individualized support to address physical barriers, such as low milk supply and latch difficulties. Full article