Search Results (881)

Mobile robot navigation remains challenging when fast convergence, collision avoidance and deployability must be satisfied simultaneously. The original Q-learning with Artificial Potential Field (QAPF) paradigm is extended in this paper with three coordinated mechanisms that together yield a reported-horizon convergence reduction of approximately four orders of magnitude (from $\sim 3\times {10}^6$ episodes to $\sim 200$ to 230 episodes under the present protocol) and an internal-ablation collision-rate reduction of approximately one order of magnitude ($6.2\%$ to $0.3\%$), and that open a new capability frontier covering dynamic obstacles, multi-robot coordination, energy-aware velocity modulation and embedded-deployable inference timing. The first mechanism is a potential-based reward-shaping schedule whose unclipped fixed-weight form follows the policy-invariant shaping theorem, while the implemented clipped and time-varying form is used as an empirically stable approximation. Under the present experimental protocol, the reported convergence horizon is reduced from the $\sim 3\times {10}^6$ episodes reported for the original QAPF formulation to approximately 200 to 230 episodes; this comparison is protocol-dependent and is not claimed as a controlled one-to-one runtime speedup. The second mechanism is a discrete Control Barrier Function (CBF)-inspired action filter (thediscrete filter described in this paper is inspired by the continuous-time CBF literature, but does not carry a forward-invariance proof; it is used as an empirical safety mechanism rather than as a formal Control Barrier Function in the formal continuous-time sense) with per episode visit memory by which the held-out collision rate is reduced from $6.2\%$ for QAPF alone to $0.3\%$ while $93.8\%$ task completion is maintained, where this collision-rate comparison is internal to the QAPF ablation because the prior QAPF reference does not report a comparable held-out collision metric. The third mechanism is a set of extensions to dynamic obstacles, two-robot cooperative navigation under a centralized scheme (with an explicit $\mathcal{O}(N^2)$ scaling-cost analysis and three decentralization strategies for fleets beyond the small-N regime), curriculum learning and energy-aware velocity modulation. Disturbance robustness tests, empirical timeout/stagnation detection for unreachable-goal cases, i7 reference inference timing with projected embedded-device latencies, multi-axis generalization over obstacle density and grid size, scalability analysis for centralized multi-robot coordination and a scope comparison against A* and RRT* are added by the revised evaluation. Across 30 independent seeds on held-out static maps, $94.5\pm 2.1\%$ success is achieved by adaptive QAPF while $93.8\pm 2.3\%$ success with $0.3\pm 0.4\%$ collisions is achieved by QAPF+CBF. Under a separate finite robustness suite, $85.0\pm 4.1\%$ success is retained by QAPF+CBF in the combined disturbance regime. The timing study indicates that the 20 Hz real-time threshold is comfortably exceeded by all methods on the measured i7 reference platform and by all projected embedded-device equivalents. The results show that a lightweight and safety-oriented navigation policy for grid-based mobile-robot settings can be provided by APF-guided tabular reinforcement learning when it is paired with a discrete safety filter and a clarified energy and robustness analysis. Full article

In panel-furniture manufacturing, dynamic scheduling is challenging because upstream motherboard release and parallel CUT-machine assignment are tightly coupled with downstream multibranch processing, finite-capacity buffers, and machine disturbances. This study investigates an event-driven scheduling problem in a multibranch panel-furniture workshop and aims to minimize the cumulative tardiness of all parts. To capture the structural dependencies of the shop floor, we construct a heterogeneous graph to represent the real-time production state. In this graph, candidate motherboards, parallel CUT machines, and downstream branch-level production units form different node types. The representation explicitly encodes motherboard–branch coupling features and machine–branch inflow relations, while aggregated branch-state features summarize detailed process-order information within each downstream branch. Based on this structured state representation, we develop a proximal policy optimization (PPO)-based actor–critic framework to learn adaptive motherboard release and CUT-assignment policies. We compare the proposed method with three heuristic rules and two PPO-based learning baselines under different machine-availability levels, buffer-capacity settings, and dataset scales. The results show that the proposed method achieves the best overall tardiness performance across the tested baselines and converges faster and more stably than the other PPO-based methods under different dataset scales and workshop conditions. These findings demonstrate the effectiveness of combining heterogeneous graph modeling with reinforcement learning for dynamic scheduling in multibranch panel-furniture production systems. Full article

This paper proposes a finite-time target tracking control for multi-rotor unmanned aerial vehicles (UAVs) based on a vision-inertial-stabilized platform. To address the challenge of stable and accurate moving target tracking, the sliding mode control (SMC) technique is used to overcome limitations of conventional control algorithms, such as poor robustness and slow convergence speed. First, by computing the pixel deviation between the target and the image center, a kinematic model of the tracking target is established. Then, by introducing homogeneous system theory into the sliding mode surface design, a non-singular fast integral terminal sliding mode control (NFITSMC) is designed for target tracking via regulating the rotational angular acceleration of dual actuators in the vision inertial stabilized platform, thereby driving the pixel deviation to converge to zero in a finite time. Strict theoretical analysis is given to prove the finite-time stability and robustness of the closed-loop control system. Furthermore, simulation results demonstrate that the proposed method maintains higher tracking accuracy than SMC, ISMC, and TSMC. Full article

The scientific literature increasingly supports the use of computational models to predict fracture across a wide range of applications, which, when calibrated with experimental data, can yield highly consistent results. Although the extended finite element method (XFEM) is widely used in commercial packages, phase field (PF) methods have emerged as a robust alternative. In this study, a cohesive zone model (CZM) was implemented using both approaches (a PF model with an implicit damage initiation criterion and a standard commercial XFEM solver with an explicit damage initiation criterion) to analyze their robustness and computational efficiency. First, a standardized fracture test of a compact tension (CT) specimen was simulated and compared with experimental data to validate both methods, achieving accurate predictions under plane strain conditions with a dominant mode I fracture behavior. Subsequently, the application of both fracture models was extended to flexible thoracic prostheses across two distinct chest wall reconstruction scenarios: a single-rib unilateral model and a multi-rib bilateral configuration. An extreme-case compressive displacement was assessed to identify critical regions susceptible to fracture initiation and to evaluate the structural limits of the proposed designs. The results showed that the PF approach required a higher computational time, but exhibited more stable convergence. In contrast, the XFEM-based solver required careful mesh calibration to ensure convergence under complex conditions. These results highlight the potential of the PF approach as a practical tool for identifying and improving critical regions of implants, overcoming the limitations of commercial XFEM implementations. Full article

This paper presents a novel distributed coordination framework for multi-agent robotic swarms tailored for smart transportation applications. The proposed approach addresses the critical pre-transportation phase where a fleet of mobile robots, eventually with different sizes, must converge to fixed positions around an object to ensure effective caging within a user-defined prescribed time. By leveraging a time-varying diffeomorphic mapping based on an affine transformation, the strategy embeds prescribed-time guarantees within a swarm-inspired framework that maps agents between virtual and real reference frames. This methodology ensures the simultaneous achievement of precise target convergence, finite-time stability regardless of initial conditions, and inherent collision avoidance by explicitly considering the physical footprint of each robotic unit. The control protocol is first derived for scalar systems and subsequently extended to multidimensional robotic fleets using additional diffeomorphism-based techniques, which allow for the management of multiple non-interacting swarms to reduce network communication overhead. Full article

This study develops an effective numerical method for addressing the time-fractional gas dynamics equation formulated with the Caputo time-fractional derivative. Novel basis functions are utilized, formulated as particular generalized Fibonacci polynomials contingent on a free parameter. This family generalizes the second kind of Chebyshev family. For the proposed polynomials, we establish basic analytical properties, including closed-form series expansion, inverse relation, moment and linearization formulas, and operational matrices for both integer-order and Caputo fractional derivatives. Using these tools, the fractional model, together with its underlying conditions, can be transformed into a finite system of nonlinear algebraic equations via a collocation strategy. Using Newton’s iterative method, the resulting system can be treated. A full convergence analysis of the double generalized Chebyshev expansion is provided. We demonstrate the accuracy and reliability of the presented method through several numerical simulations. Comparisons with existing numerical methods show that this approach achieves higher accuracy and faster execution. Full article

This study presents an application-oriented CFD–FEM integrated workflow for analyzing chamber-side field non-uniformity and kelp-side thermal response during infrared drying. A three-dimensional steady-state CFD model was first established to reconstruct the chamber temperature, airflow, and incident radiation fields under certain operating conditions. Numerical consistency was checked through residual convergence; monitored variables; and global mass balance, for which the net mass imbalance was 0.004077 kg s⁻¹. The reconstructed mid-plane fields were then processed in MATLAB to extract the mean values, extrema, and coefficients of variation, and a composite objective function was used to screen the tested operating conditions in terms of field uniformity, temperature band compliance, and overheating risk. The thermal loads obtained via CFD were subsequently mapped onto a kelp finite element model to simulate the transient surface temperature evolution. Among the tested cases, case01 yielded the lowest composite objective value (J = 0.4535); its mapped kelp response showed a mean surface temperature of 62.23 °C and a maximum temperature of 63.57 °C at the exported time step. The proposed framework is therefore suitable for thermal response assessment and operating condition screening, although determining the full drying behavior still requires coupling of moisture transfer and improved experimental validation. Full article

To address the deteriorated control performance of permanent magnet synchronous motor (PMSM) drive systems for new energy vehicles (NEVs) under complex conditions caused by multi-source disturbances (internal parameter perturbations and external load mutations), this paper proposes an improved terminal integral sliding mode control (ITISMC-ADERL) strategy integrating a piecewise adaptive terminal integral sliding mode surface and an ADERL. The proposed sliding mode surface adopts interval-adaptive switching between high- and low-order power terms, completely eliminating singularity and integral saturation defects of traditional terminal sliding mode control while ensuring fast convergence, and achieving an optimal structural balance between convergence speed and chattering suppression. The state-dependent ADERL leverages the synergy of error-sliding variable coupled dynamic gain adjustment and variable exponential power compensation, realizing dual-mode adaptive switching of “strong driving for fast approaching far from the sliding surface, weak gain for smooth regulation near the sliding surface”, which significantly improves control accuracy and anti-disturbance robustness. The finite-time convergence of the closed-loop system is rigorously proved via Lyapunov stability theory. Full-operating-condition comparative tests on a TMS320F28379D DSP platform show that the proposed strategy outperforms SMC-ERL, ISMC-ERL and ITISMC-ERL in all test scenarios (no-load startup, acceleration/deceleration, sudden load changes, flux linkage perturbation), meeting the requirements of high-performance NEV drive systems and possessing important engineering application potential. Full article

Aiming at the problems of cumbersome parameter tuning and low computational efficiency in traditional methods for the bandgap optimization of periodic thin-walled stiffened coupled structures, this paper integrates the null-space method with the Kirchhoff thin-plate theory to establish an efficient model for bandgap analysis. The proposed method realizes matrix-based construction of coupled and periodic boundary conditions, decouples boundary constraints from displacement shape functions, avoids the limitations of virtual spring stiffness, and requires no remeshing during parameter variation. Comparisons with the finite element method verify its convergence and accuracy: the average deviation of bandgap widths in the 0–250 Hz range is 0.37 Hz, and the computational efficiency is about 2.5 times that of FEM(Finite Element Method). This paper also systematically analyzes the effects of four key parameters, including thin-wall thickness, stiffener thickness, stiffener height and stiffener spacing, on the number and width of bandgaps and proposes targeted optimization strategies for different engineering scenarios. The results provide a new method for vibration and noise reduction design of such structures and lay a foundation for future bandgap modeling and optimization of advanced lightweight periodic structures. Full article

This paper proposes a novel adaptive sliding mode control strategy for bionic aerial manipulators performing canopy-sampling tasks. Specifically, an adaptive neural barrier function-based fast terminal sliding mode control (BFASMC-NN) scheme is developed to address the joint-space trajectory tracking problem by integrating fast continuous nonsingular terminal sliding mode control (FNTSMC), neural networks (NNs), and barrier functions (BFs). The aerial manipulator is modeled as a rootless system, and its kinematic and dynamic characteristics are analyzed separately. Radial basis function neural networks (RBF-NNs) are introduced to approximate lumped disturbances, while BFs are incorporated to mitigate the effects of joint input saturation. Meanwhile, FNTSMC is employed to guarantee finite-time convergence of the system states. The stability of the closed-loop system is rigorously proven based on Lyapunov stability theory. Two simulation studies are conducted to validate the proposed method, and the results demonstrate that it achieves stronger disturbance rejection capability, faster convergence, and higher tracking accuracy than existing approaches. Full article

Aiming at solving the problems of severe chattering, irreconcilable convergence speed, and steady-state accuracy in traditional sliding-mode control (SMC) for the speed regulation system of permanent magnet synchronous motors (PMSMs) in rail transit traction, as well as its poor adaptability to complex disturbances such as frequent acceleration/deceleration and sudden load changes under traction conditions, a sliding-mode control strategy integrating improved piecewise terminal integral sliding-mode control (IPTISMC) with an adaptive smooth exponential reaching law (ASERL) is proposed. Taking the surface-mounted PMSM for rail transit traction as the research object, the d-q axis mathematical model is established, and a terminal integral sliding surface with a piecewise nonlinear function is designed, which resolves the problems of complex solutions and steady-state errors of the traditional sliding surface through a piecewise cooperative mechanism for large and small error stages. The designed ASERL realizes adaptive gain adjustment based on the state variables of the sliding surface and replaces the sign function with the hyperbolic tangent function, thus alleviating the inherent contradiction between convergence and chattering in the fixed-gain reaching law. The global stability and finite-time convergence of the system are rigorously proved based on Lyapunov stability theory. Furthermore, comparative experiments involving no-load operation, acceleration and deceleration, sudden load application and removal, and parameter perturbation are carried out on a DSP experimental platform for SMC-ERL, ISMC-ERL, IPTISMC-ERL and the proposed IPTISMC-ASERL. Experimental results show that the proposed IPTISMC-ASERL strategy can significantly improve the dynamic response and steady-state control accuracy of the PMSM speed regulation system for rail transit traction, effectively suppress chattering to enhance riding comfort, and simultaneously strengthen the system’s anti-disturbance capability and parametric robustness. It can fully meet the engineering control requirements for high precision and high stability of PMSMs in rail transit traction applications. Full article

This paper presents a closed-form tracking and collision avoidance framework for multi-agent differential drive robots. Existing reactive methods often rely on purely geometric proximity, leading to conservative detours and local minima. A state-dependent adaptive avoidance strategy is developed to dynamically modulate repulsive forces using the time-derivative of fractional barrier risk functions, alleviating unnecessary evasive maneuvers. Within a convergence vector field (CVF) architecture, an active safety-corrected tracking mechanism orthogonally strips hazardous velocity projections from the spatial error. This mitigates the inherent conflict between target tracking and obstacle repulsion. A matrix projection-based Lyapunov approach demonstrates the finite-time convergence of the vehicle orientation, bounded tracking errors, and collision-free properties of the closed-loop system, with effectiveness further validated through simulations. Full article

Permanent magnet synchronous motor (PMSM)-driven position servo systems in high-speed flight vehicles face severe challenges from extreme thermal environments, which induce significant parameter variations up to 25% (e.g., motor torque constant) and complex multi-scale disturbances. This paper proposes a novel adaptive robust control strategy integrating three key components: (1) an ultra-local model formulation motivated by physically consistent thermal effect analysis of electromagnetic, mechanical, and tribological parameters; (2) a dual-layer disturbance observer architecture comprising a third-order finite-time convergent extended state observer (FTCESO) for fast-varying disturbances and a σ-modification adaptive estimator for slow-varying thermal drifts; and (3) a global nonlinear integral terminal sliding mode controller with a cycloidal reaching law. Stability analysis based on homogeneous system theory and Lyapunov methods establishes practical finite-time convergence with explicit bounds. The experimental results on a TMS320F28335-based servo platform demonstrate that the proposed method reduces the maximum position deviation by 83–94% compared to PID, LADRC, and conventional SMC controllers under the tested disturbance conditions, achieving settling time reductions exceeding 90%. Under combined thermal drift and external loading, the proposed approach limits the maximum tracking error to below 0.45° while maintaining a steady-state error under 0.08°. Full article

To mitigate low-speed speed oscillations in permanent magnet synchronous motors (PMSMs) arising from the combined effects of rotor-position-related periodic disturbances and external perturbations, this paper develops a composite robust speed regulation scheme that integrates non-singular terminal sliding mode control (NTSMC) with angle-domain iterative learning control (ILC). First, a non-singular terminal sliding mode speed controller is established to remove the singularity inherent in conventional terminal sliding mode formulations while preserving finite-time error convergence. To further improve robustness and reduce chattering, an enhanced generalized super-twisting reaching law incorporating a continuous saturation function is introduced. Second, to compensate for periodic disturbances associated with rotor position, an angle-domain ILC law is constructed to iteratively learn the periodic speed-tracking error, thereby suppressing low-speed speed ripple. Meanwhile, an extended state observer (ESO) is incorporated to estimate aperiodic disturbances online, enabling coordinated rejection of disturbances with different temporal characteristics. Experimental results demonstrate that the proposed composite strategy effectively weakens the dominant harmonic components in speed fluctuation and enhances low-speed operational smoothness, confirming the effectiveness of the developed method. Full article

The energy-intensive nature of primary aluminum production necessitates advanced computational tools for process optimization. This study presents a coupled electro-thermal model of an aluminum reduction cell, developed within the framework of smart manufacturing. Using the finite difference method (FDM) implemented in MATLAB R2025b, the model resolves the three-dimensional configuration of a cell with eight prebaked anodes across four distinct physical domains (electrolyte, anodes, cathode, and gas phase). The computational grid comprises approximately 45,000 nodes with refined vertical resolution (Δz = 0.025 m) in the interelectrode gap. The electrostatic solution converges within 150–200 iterations using successive over-relaxation (SOR, ω = 1.5), with a total runtime under 15 min for 30,000 s of simulated physical time on a standard desktop workstation. Simulation results reveal characteristic temperature profiles with maxima reaching 1150 °C and a thermal uniformity index of approximately 130 °C across the central cross-section. The predicted specific energy consumption of 14.0 MWh/t Al aligns with industrial benchmarks. This computationally accessible virtual testbed enables rapid assessment of design modifications and process parameters, supporting the goals of energy efficiency and enhanced operational stability in primary aluminum production. Full article