Search Results (3,601)

Accurate pose estimation is crucial for reliable docking and recovery of Autonomous Underwater Vehicles (AUVs). Traditional visual-based pose estimation methods face inherent challenges: monocular methods often struggle with depth inference, and conventional Perspective-n-Point (PnP) algorithms exhibit accuracy degradation at large viewing angles and limited noise resistance, while binocular systems involve higher computational complexity. This paper proposes a two-stage algorithm that combines iterative PnP initialization with binocular constraint optimization. By using iterative PnP to establish reliable initial estimates, the approach avoids convergence difficulties of direct binocular optimization, while the subsequent binocular refinement leverages stereo geometric constraints to enhance accuracy. Comprehensive evaluation through simulation, land-based experiments, and underwater validation demonstrates consistent performance improvements over conventional geometric methods. In simulation experiments across $-60°$ to $60°$ yaw angles, the method achieves 93.2% and 28.6% improvements in translation and rotation accuracy respectively compared to iterative PnP. Land-based validation confirms 32.7% average rotation error reduction, while underwater experiments demonstrate 76.5% average distance error reduction under real optical conditions including refraction and light attenuation. The method maintains real-time processing capability (2.16 ms per frame), offering a practical solution for AUV pose estimation in docking applications. Full article

In Orthogonal Time–Frequency Space (OTFS) systems, signal detection algorithms based on convolutional neural networks (CNNs) suffer from insufficient feature extraction and are limited by local mixing. Additionally, fixed convolution kernels struggle to match the sparsity and non-stationary characteristics of OTFS signals in the delay-Doppler domain, resulting in slow convergence and high training costs. We do not stop at simply integrating more features outside the existing CNN framework. Instead, we go deeper into the network and replace the fixed convolution kernels with wavelet convolution layers that have time–frequency-adaptive capabilities. This fundamental change allows the network to more intrinsically match the physical characteristics of OTFS signals in the delay-Doppler domain, thereby achieving excellent detection performance while also gaining faster convergence efficiency. Therefore, this paper proposes a signal detection method using an adaptive wavelet convolutional neural network (AWCNN). The approach replaces the first convolutional layer of a standard CNN with an adaptive wavelet layer, which leverages the time–frequency localization properties of Sym4 wavelet kernels along with learnable scaling and translation factors. This enhances the network’s ability to extract sparse features from OTFS signals. Additionally, the model incorporates both the original received signal and preliminary estimates from the message-passing (MP) algorithm as input features, enriching the dataset and further improving detection performance. Experimental results demonstrate that the AWCNN model achieves superior convergence efficiency compared to the CNN model, which attains a bit error rate (BER) comparable to that of the CNN algorithm at a low signal-to-noise ratio of 2 dB, operating without the need for pilot-assisted channel state information acquisition. Full article

To address the collaborative optimization of efficiency, stability, and energy consumption in container part-handling operations of material-handling robots, this paper proposes a multi-objective trajectory-planning method. First, the kinematic and dynamic models of the robot are established based on the improved D-H parameter method and Lagrange method, with the coordinates of key interpolation points and joint angles in handling operations clarified. Subsequently, the 3-5-3 hybrid polynomial interpolation method is adopted to generate the trajectory. Optimizing the objectives of minimum time, minimum jerk, and minimum energy consumption, an improved particle swarm optimization (IPSO) algorithm dynamically adjusts the inertia weight and learning factor for trajectory optimization. The results show that the convergence speed of the IPSO algorithm increases by 39.6% on average, and the fitness value reduces by 12.7% on average. Experimental validation of joint trajectory optimization demonstrated maximum positional errors of approximately 0.0049 rad, 0.0005 rad, 0.005 rad, and 0.0049 rad for the four joints, with the experimental trajectory closely matching the planned trajectory. Finally, the effectiveness of the scheme is verified by MATLAB 2019 and Adams simulation. Under the time–jerk–energy optimization strategy, the joint trajectory is continuous and smooth, with the peak jerk reduced by 30–40% and the peak torque reduced by 5–10%. The comprehensive performance is superior to the single-objective and dual-objective optimization strategies. This research provides technical support for the efficient and stable operation of the handling robot and provides a reference for the trajectory planning of similar robots. Full article

Time-varying disturbances arising from complex terrain and the lack of rigorous constraint-handling mechanisms significantly degrade the path-tracking performance of unmanned agricultural tractors (UATs). To address these issues, this paper proposes a generalized extended-state-observer-based prescribed-performance sliding-mode (GESO-PPSM) control method. First, a homeomorphic mapping-based prescribed performance function is employed to impose hard performance constraints, guaranteeing that the preview error remains within predefined bounds throughout the entire process. Second, a generalized super-twisting extended-state observer (GESO) is developed to compensate for lumped uncertainties, enabling finite-time and high-accuracy disturbance estimation compared with that of conventional observers. Furthermore, a switching sliding mode surface is designed to achieve fast convergence far from equilibrium while effectively suppressing overshoot near the origin. Unlike traditional sliding mode control, a continuous path-tracking control law based on a power function is formulated to ensure robustness while avoiding discontinuities. Comparative co-simulations based on a high-fidelity UAT model demonstrate that the proposed control method achieves superior steady-state accuracy, with significant reductions in preview error standard deviations of up to 92.52%, 84.33%, and 80.44% compared to PID, model predictive control (MPC), and GESO-based conventional sliding mode (GESO-SM) control, respectively. These results validate the superiority of the GESO-PPSM method in terms of accuracy, robustness, and strict constraint satisfaction in complex agricultural environments. Full article

The study addresses trajectory tracking control for underactuated vessels with uncertain backlash-type hysteresis. First, an improved practical L1-based guidance strategy is developed by embedding the L1 mechanism into the virtual ship framework to eliminate steering overshoot and yaw angle error accumulation, which can facilitate the smooth turning of ships along waypoint-based paths with large curvature. Next, to mitigate control performance degradation induced by backlash-like hysteresis nonlinearity, an improved quadratic function is utilized to boost the closed-loop system’s convergence capability. Moreover, system model uncertainty-induced perturbations are compensated using the resilient neural damping method, which can simplify the structure and reduce the computation burden of the proposed controller. Utilizing Lyapunov-based approaches and the special Young’s inequality, uniformly ultimately bounded stability over a semi-global domain is established. Finally, numerical simulations are executed to validate the efficacy of the developed control architecture. Full article

The objective of this work is to propose a simulation strategy for production planning that is compatible with the dynamism of natural gas processing, especially under an open-market arrangement, in which several scheduling simulations must be performed within short time horizons. In such contexts, traditional first-principles-based approaches, although accurate, require prohibitive computational times, motivating the need for an alternative simulation strategy. This work thus proposes a data-driven model built with the aid of machine learning and applied in a case study with historical data from the largest gas processing site in Brazil: Cabiúnas Petrobras asset. Main plant flowrates were selected: 18 targets and 44 input candidates—1282 observations from three and a half years of operation. Principal Component Analysis was used for order reduction, keeping the 22 main principal components. A forward neural network (2 hidden layers and 225 neurons per layer) was built from training/test sets randomly selected and optimized hyperparameters—learning rate (0.001533) and batch size (8). Training converged in roughly 200 epochs (Adam optimizer), with early stop triggered by the validation set. A mean absolute error of 0.0017 (test set) and R² = 0.72 were found, a promising result considering plant complexity and data simplicity. Results showed a particularly good fit for lighter products (sales gas and natural gas liquid), also indicating an opportunity for further work by including inputs related to liquid fractionation. Full article

Electrical Discharge Machining (EDM) enables precision post-weld finishing of AISI 304 stainless steel, but stochastic spark overlaps make the fatigue-critical maximum peak-to-valley height (R_max) difficult to predict. This study develops a validated physics-based framework quantifying how crater overlap governs R_max evolution. Experiments on unwelded AISI 304 cylinders—proxying weld metal while excluding heat-affected zone (HAZ) effects—used Central Composite Design (20 trials, 900–9380 μJ discharge energies). Profilometry and scanning electron microscopy (SEM) correlated the crater size, overlap intensity, micro-cracking, and R_max escalation from 18 to 85 μm. Primary and secondary crater formation under minimum and maximum overlap configurations were simulated using a 2D axisymmetric finite element model with Gaussian heat flux and temperature-dependent thermophysical properties. The predictive metric R_max,num = (d_initial + d_secondary)/2 achieved 11–19% average error against the experimental R_max,exp, with complementary valley depth (R_v) validation at 13% error. The Specimen 7 outlier (~50% error) reveals the limitations of deterministic modelling under stochastic debris accumulation and plasma instability at intermediate energies. Crater overlap generates secondary dimples, sharp inter-crater peaks, and rim micro-crack networks, driving the 4.7-fold R_max increase—approaching International Institute of Welding (IIW) fatigue thresholds (<25 μm for high-cycle categories). The framework explicitly links the discharge energy, plasma channel radius (R_pc), and overlap geometry to surface topography, enabling process optimization (I·t_on < 60 A·s maintains R_max < 25 μm). Mesh independence (<2.5% convergence) and six centre-point replicates (CV = 4.2%) confirm robustness. This validated upper-bound R_max predictor supports the digital co-optimization of welding and EDM parameters for aerospace/energy applications, with planned extensions to stochastic 3D models incorporating adaptive remeshing and real weld topographies. Full article

This paper proposes an integrated finite-time relative-threshold event-triggered control (FTRTETC) framework for unmanned surface vehicle (USV) formations under input saturation and unknown time-varying external disturbances. Firstly, a scheme of USV formation control based on signed graph theory is proposed. Next, a Gaussian error function is used to handle input saturation and simplify the backstepping design. Then, a finite-time formation controller is developed based on the active disturbance rejection control (ADRC) method with extended state observers (ESOs) and tracking differentiators (TDs). Also, a relative-threshold event-triggered mechanism is designed to reduce the frequency of control execution and communication load. By Lyapunov’s stability theory, the proposed controller is proven to achieve finite-time convergence, ensuring all closed-loop signals achieve global uniform ultimate boundedness (GUUB) and the system is without Zeno behaviour. Finally, numerical simulation examples are presented to validate the effectiveness and robustness of the proposed controller. Full article

The Electro-Hydraulic Servo Actuator for Active Suspensions (ASEHSA) plays a decisive role in shaping the holistic performance of vehicle suspension systems through its dynamic response speed and control precision. However, achieving high-performance control of ASEHSA still faces challenges. On one hand, existing model-based control methods are highly sensitive to parameter uncertainties and unmodeled nonlinear hydraulic dynamics, which can easily lead to reduced robustness in practical applications. On the other hand, traditional model-free strategies have limited time-delay compensation capabilities and often struggle to balance overshoot and settling time under delayed and disturbed conditions. To resolve this challenge, this study proposes an improved model-free adaptive control method that incorporates the differentiation of the tracking error (DE-IMFAC). Within the framework of traditional model-free adaptive control (MFAC), this approach reconfigures the time-delay term from an explicit form in the control law to implicit management, substantially mitigating the influence of time delays on system control performance. At the same time, by refining the performance criterion function and integrating a tracking error differentiation term together with dynamic weighting factors, the dynamic performance and adjustment flexibility of the controller are significantly enhanced. Additionally, by leveraging the characteristic equation of discrete autonomous systems and compression mapping theory, the BIBO stability of the DE-IMFAC control system and the monotonic convergence of the tracking error are rigorously established through theoretical analysis. Simulation and experimental results demonstrate that, compared with PID and traditional MFAC methods, DE-IMFAC significantly reduces integral absolute error, overshoot, settling time, and maximum position tracking error, while improving disturbance rejection capability. This approach does not depend on an accurate mathematical model of the ASEHSA system and maintains robust dynamic performance under complex operating environments characterized by time delays and data disturbances, providing a practical solution for ASEHSA and related industrial control systems. Full article

Parameter self-tuning of servo control systems is crucial for optimizing automation processes, especially in complex systems such as permanent magnet synchronous motors. In this paper, a nonlinear adaptive whale optimization algorithm (NAWOA) is proposed and applied to parameter self-tuning, which improves the traditional whale optimization algorithm (WOA) by nonlinearly adaptively adjusting two parameters during optimization to enhance fast convergence and global search capabilities. A servo control system with three parameters to be tuned is constructed using both simulation and physical methods. Simulation and experimental results show that the NAWOA outperforms the genetic algorithm, particle swarm optimization, and WOA in parameter self-tuning of the servo control system with lower error indicators and fast convergence speed. Although it still faces the challenge of initial condition dependency, the proposed NAWOA provides a powerful solution for real-time industrial applications. Full article

To address the task-grouping problem for air–ground integrated Unmanned Aerial Vehicle (UAV) swarm missions in three-dimensional (3D) environments, this study proposes a data-preprocessing and hybrid initialization clustering method based on 3D spatial features. A dual-modal prototype meta-heuristic optimization model, Dual-Prototype Metaheuristic K-Means (DPM-Kmeans), is constructed accordingly. First, to overcome spatial information loss in high-dimensional task allocation, a 3D spatial task data preprocessing technique and a hybrid initialization strategy based on the golden spiral distribution are designed. This ensures the diversity and environmental adaptability of the initial solutions. Second, a dual-modal prototype optimization framework incorporating row prototypes (local refinement) and column prototypes (global combination) was constructed using meta-heuristics and clustering algorithms. The prototype-driven replacement update mechanism simultaneously performs global and local search, balancing the algorithm’s exploration and exploitation capabilities while expanding the solution space. This effectively addresses premature convergence issues in complex search spaces. Simultaneously, a collaborative multi-constraint, dynamically weighted optimization model was constructed, incorporating task requirements and flight distance constraints to ensure that the grouping scheme approximates the global optimum. Simulation results demonstrate that compared to traditional K-means and mainstream meta-heuristic optimization algorithms, DPM-Kmeans achieves an overall improvement of 2–10% in Sum of Squared Errors (SSE), Silhouette Coefficient (SC), and Davies–Bouldin Index (DB) metrics. It exhibits superior convergence speed and solution quality, proving the method’s excellent scalability and robustness in multi-constraint, large-scale 3D scenarios. Full article

Differential wavefront sensing (DWS) is widely adopted for high-precision angular detection in interferometric systems, yet its measurement range is constrained by the nonlinear implicit phase–angle relationship. This paper proposes a strategy-enhanced differential evolution algorithm, termed Bi-inheritance and Tournament-Selection-based Differential Evolution (BiTsDE), to suppress nonlinear angular errors. The method introduces fitness-guided inheritance of mutation and crossover factors and tournament-based elite parent selection, enabling adaptive balance between global exploration and local exploitation. Unlike conventional DE variants that mainly tune control parameters, BiTsDE optimizes the evolutionary search strategy, enhancing early-stage diversity and late-stage convergence stability. Simulations demonstrate angular resolution better than 0.06 nrad within ±1 mrad. Experiments show that up to 600 μrad, BiTsDE reduces demodulation error by 99.9% compared with linear DWS, achieving 17.9 nrad precision and 42% faster convergence. These results validate BiTsDE as an effective solution for nonlinear error suppression in DWS-based high-precision optical metrology, particularly for space-based gravitational wave detection. Full article

The primary objective of this research was to systematically investigate how bond–slip behavior affects the flexural behavior of alkali-activated slag concrete (AASC) beams reinforced with steel fibers. To this end, a finite element model incorporating the steel–concrete interface bond–slip effect was formulated in Abaqus using a separated modeling approach, grounded in a thorough analysis of established bond–slip constitutive models. Numerical simulations were conducted on both reinforcing bar pull-out specimens and beam members to examine the bond–slip interaction between steel reinforcement and steel fiber-reinforced alkali-activated slag concrete (SFR-AASC), as well as its influence on the flexural behavior of the beams. The results indicate that the bond–slip interaction at the steel–concrete interface can be effectively captured using nonlinear spring elements. The proposed modeling approach is simple to implement and demonstrates stable numerical convergence. For the pull-out specimens, the numerically obtained stress contours along the loading direction, together with the corresponding load–displacement curves, show good agreement with experimental observations. Further comparisons between numerical predictions and experimental results for beam specimens reveal that the prediction errors of the fully bonded model range from 0.2% to 9.7%, whereas those of the model accounting for bond–slip effects are reduced to 0.1–4.7%. The bond–slip model provides more accurate predictions of cracking load, ultimate load, and overall load–displacement behavior, thereby verifying the validity and accuracy of the developed finite element modeling strategy. Full article

The temperature regulation of nonlinear continuous stirred tank reactor (CSTR) processes remains a challenging control problem due to strong nonlinearities, time-delay effects, and sensitivity to disturbances and parameter variations. Conventional proportional–integral–derivative (PID)-based control strategies often fail to provide the robustness and precision required under such conditions, motivating the use of more flexible controller structures and advanced optimization techniques. In this study, an enhanced joint-opposition artificial lemming algorithm (JOS-ALA) is proposed for the optimal tuning of a fractional-order PID (FOPID) controller applied to CSTR temperature control. The proposed JOS-ALA incorporates a joint opposite selection mechanism into the original ALA to improve population diversity, convergence stability, and resistance to local optima stagnation. A nonlinear CSTR model is linearized around a stable operating point, and the resulting model is employed for controller design and optimization. The FOPID controller parameters are tuned by minimizing a composite cost function that simultaneously accounts for tracking accuracy, overshoot suppression, and instantaneous error behavior. The effectiveness of the proposed approach is assessed through extensive simulation studies and benchmarked against state-of-the-art and high-performance metaheuristic optimizers, including ALA, electric eel foraging optimization (EEFO), linear population size reduction success-history based adaptive differential evolution (L-SHADE), and the improved artificial electric field algorithm (iAEFA). The benchmarking set is further extended with the success rate-based adaptive differential evolution variant (L-SRTDE) to broaden the comparative evaluation. Simulation results demonstrate that the JOS-ALA-based FOPID controller consistently achieves superior performance across multiple criteria. Specifically, it attains the lowest mean cost function value of 0.1959, eliminates overshoot, and yields a normalized steady-state error of 4.7290 × 10⁻⁴. In addition, faster transient response and improved robustness under external disturbances and measurement noise are observed when compared with competing methods. Statistical reliability of the observed performance differences is additionally examined using a Wilcoxon signed-rank test conducted over 25 independent runs. The resulting p-values confirm that the improvements achieved by the proposed approach are statistically significant at the 5% level across all pairwise algorithm comparisons. These findings indicate that the proposed JOS-ALA provides an effective and reliable optimization framework for high-precision temperature control in nonlinear CSTR systems and offers strong potential for broader application in complex process control problems. Full article

This article proposes a novel predefined-time sliding mode fault-tolerant control method for the attitude tracking of hypersonic vehicles subject to actuator failures and external disturbance. A novel sufficient condition of the Lyapunov function ensuring predefined-time stability and practical predefined-time stability is established, which serves as the theoretical basis for the controller design. In contrast to existing Lyapunov conditions, this formulation provides greater design flexibility. Based on this theoretical foundation and an extended state observer, a predefined-time sliding mode controller is developed. The controller ensures system robustness while enabling an accurate estimate of the settling time upper bound, which is independent of initial conditions. Furthermore, the actual settling time can be tuned via the preset parameters. Finally, the proposed controller is evaluated on a hypersonic vehicle model subject to actuator bias, loss of effectiveness faults, and external disturbance. Numerical simulations demonstrate that the proposed method exhibits superior performance, including faster convergence, lower tracking errors, and enhanced robustness and fault tolerance, compared to an existing predefined-time sliding mode control approach. Full article