Engineering Applications of Artificial Intelligence最新文献_第3页

The docking control system of an autonomous underwater vehicle combining intelligent object recognition and deep reinforcement learning 结合智能物体识别和深度强化学习的自主潜水器对接控制系统

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-01 DOI: 10.1016/j.engappai.2024.109565

This study develops a visual-based docking system (VDS) for an autonomous underwater vehicle (AUV), significantly enhancing docking performance by integrating intelligent object recognition and deep reinforcement learning (DRL). The system overcomes traditional navigation limitations in complex and unpredictable environments by using a variable information dock (VID) for precise multi-sensor docking recognition in the AUV. Employing image-based visual servoing (IBVS) technology, the VDS efficiently converts 2D visual data into accurate 3D motion control commands. It integrates the YOLO (short for You Only Look Once) algorithm for object recognition and the deep deterministic policy gradient (DDPG) algorithm, improving continuous motion control, docking accuracy, and adaptability. Experimental validation at the National Cheng Kung University towing tank demonstrates that the VDS enhances control stability and operational reliability, reducing the mean absolute error (MAE) in depth control by 42.03% and pitch control by 98.02% compared to the previous method. These results confirm the VDS's reliability and its potential for transforming AUV docking.

本研究为自主潜水器（AUV）开发了基于视觉的对接系统（VDS），通过集成智能物体识别和深度强化学习（DRL），显著提高了对接性能。该系统利用可变信息停靠点（VID）对自动潜航器进行精确的多传感器停靠识别，从而克服了复杂和不可预测环境中的传统导航限制。VDS 采用基于图像的视觉伺服（IBVS）技术，可有效地将二维视觉数据转换为精确的三维运动控制指令。它集成了用于物体识别的 YOLO（You Only Look Once 的缩写）算法和深度确定性策略梯度（DDPG）算法，提高了连续运动控制、对接精度和适应性。在成功大学拖曳坦克上进行的实验验证表明，VDS 增强了控制稳定性和运行可靠性，与以前的方法相比，深度控制的平均绝对误差（MAE）减少了 42.03%，俯仰控制减少了 98.02%。这些结果证实了 VDS 的可靠性及其改变 AUV 停靠的潜力。

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引用次数: 0

Latent temporal smoothness-induced Schatten-p norm factorization for sequential subspace clustering 用于序列子空间聚类的潜在时间平滑性诱导 Schatten-p norm 因式分解法

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-01 DOI: 10.1016/j.engappai.2024.109476

This paper presents an innovative latent temporal smoothness-induced Schatten-

p

norm factorization (SpFLTS) method aimed at addressing challenges in sequential subspace clustering tasks. Globally, SpFLTS employs a low-rank subspace clustering framework based on Schatten-2/3 norm factorization to enhance the comprehensive capture of the original data features. Locally, a total variation smoothing term is induced to the temporal gradients of latent subspace matrices obtained from sub-orthogonal projections, thereby preserving smoothness in the sequential latent space. To efficiently solve the closed-form optimization problem, a fast Fourier transform is combined with the non-convex alternating direction method of multipliers to optimize latent subspace matrix, which greatly speeds up computation. Experimental results demonstrate that the proposed SpFLTS method surpasses existing techniques on multiple benchmark databases, highlighting its superior clustering performance and extensive application potential.

本文提出了一种创新的潜在时态平滑诱导 Schatten-p norm 因式分解（SpFLTS）方法，旨在解决顺序子空间聚类任务中的难题。从全局来看，SpFLTS 采用了基于 Schatten-2/3 准则因式分解的低秩子空间聚类框架，以增强对原始数据特征的全面捕捉。从局部来看，通过次正交投影得到的潜在子空间矩阵的时间梯度诱导了总变异平滑项，从而保持了序列潜在空间的平滑性。为了有效解决闭式优化问题，快速傅立叶变换与非凸交替方向乘法相结合来优化潜子空间矩阵，从而大大加快了计算速度。实验结果表明，所提出的 SpFLTS 方法在多个基准数据库上超越了现有技术，凸显了其卓越的聚类性能和广泛的应用潜力。

引用次数: 0

Adaptive graph learning algorithm for incomplete multi-view clustered image segmentation 用于不完整多视角聚类图像分割的自适应图学习算法

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-10-31 DOI: 10.1016/j.engappai.2024.109264

There are problems of relying on data initialization and ignoring data structure in existing incomplete multi-view clustering algorithms, an adaptive graph learning incomplete multi-view clustering image segmentation algorithm is proposed. Firstly, the similarity matrix of each non missing view is adaptive learned, and the index matrix of the missing view is used to complete the similarity matrix and unify the dimensions,which ensure the authenticity of the data and revealing the data structure. Secondly, the low dimension representation of the complete similarity matrix under spectral constraints is calculated, and a discrete clustering index matrix is directly obtained through adaptive weighted spectral rotation, avoiding post-processing. The clustering index matrix is used to obtain clustering of multi-view features, thereby obtaining image segmentation results. Finally, an iterative algorithm optimization model is presented, which is compared with six existing algorithms using seven evaluation metrics on six datasets. The results show significant improvements in clustering performance and segmentation performance.

针对现有不完整多视图聚类算法中存在的依赖数据初始化、忽略数据结构等问题，提出了一种自适应图学习不完整多视图聚类图像分割算法。首先，自适应学习每个非缺失视图的相似性矩阵，并利用缺失视图的索引矩阵来完成相似性矩阵并统一维度，从而保证了数据的真实性并揭示了数据结构。其次，计算完整相似性矩阵在光谱约束下的低维表示，通过自适应加权光谱旋转直接得到离散聚类索引矩阵，避免了后处理。利用聚类索引矩阵对多视角特征进行聚类，从而得到图像分割结果。最后，介绍了一种迭代算法优化模型，并在六个数据集上使用七个评价指标与现有的六种算法进行了比较。结果表明，该算法在聚类性能和分割性能方面都有明显改善。

引用次数: 0

Kinematic matrix: One-shot human action recognition using kinematic data structure 运动学矩阵：利用运动学数据结构识别一帧人类动作

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-10-31 DOI: 10.1016/j.engappai.2024.109569

One-shot action recognition, which refers to recognizing human-performed actions using only a single training example, holds significant promise in advancing video analysis, particularly in domains requiring rapid adaptation to new actions. However, existing algorithms for one-shot action recognition face multiple challenges, including high computational complexity, limited accuracy, and difficulties in generalization to unseen actions. To address these issues, we propose a novel kinematic-based skeleton representation that effectively reduces computational demands while enhancing recognition performance. This representation leverages skeleton locations, velocities, and accelerations to formulate the one-shot action recognition task as a metric learning problem, where a model projects kinematic data into an embedding space. In this space, actions are distinguished based on Euclidean distances, facilitating efficient nearest-neighbour searches among activity reference samples. Our approach not only reduces computational complexity but also achieves higher accuracy and better generalization compared to existing methods. Specifically, our model achieved a validation accuracy of 78.5%, outperforming state-of-the-art methods by 8.66% under comparable training conditions. These findings underscore the potential of our method for practical applications in real-time action recognition systems.

单次动作识别是指仅使用单个训练示例来识别人类所做动作，它在推进视频分析方面前景广阔，尤其是在需要快速适应新动作的领域。然而，现有的单次动作识别算法面临着多重挑战，包括计算复杂度高、准确性有限以及难以泛化到未见过的动作。为了解决这些问题，我们提出了一种新颖的基于运动学的骨架表示法，它能有效降低计算需求，同时提高识别性能。这种表示方法利用骨架位置、速度和加速度，将单次动作识别任务表述为一个度量学习问题，其中一个模型将运动学数据投射到一个嵌入空间。在这个空间中，动作是根据欧氏距离来区分的，这有利于在活动参考样本中进行高效的近邻搜索。与现有方法相比，我们的方法不仅降低了计算复杂度，还实现了更高的准确性和更好的泛化。具体来说，在可比的训练条件下，我们的模型达到了 78.5% 的验证准确率，比最先进的方法高出 8.66%。这些发现凸显了我们的方法在实时动作识别系统中的实际应用潜力。

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引用次数: 0

Data-driven nonmodel seismic assessment of eccentrically braced frames with soil-structure interaction 土-结构相互作用偏心支撑框架的数据驱动非模型抗震评估

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-10-31 DOI: 10.1016/j.engappai.2024.109549

This study presents a nonmodel-based machine learning framework for estimating engineering demand parameters (EDPs) of eccentrically braced frames with soil-structure interaction effects. The objective is to estimate residual and peak story drift ratio, peak floor acceleration, and develop fragility curves using traditional regression equations and advanced machine-learning techniques. Correction coefficients are developed to improve prediction accuracy by accounting for soil-structure interaction. A comprehensive database, including incremental dynamic analysis results of 4- and 8-story frames, is developed, consisting of 109,841 data points. The database includes fixed-base models and models with various soil-structure interaction values, subjected to 44 far-field ground motions. Four scenarios are introduced considering various input variables to compare the impact of soil-structure interaction. Findings reveal the effects of soil-structure interaction features on the performance of machine learning algorithms, increasing by up to 17.61% of the coefficient of determination. Utilizing the predicted story drift ratio, two types of fragility curves indicate more precise predictions, emphasizing the impact of soil-structure interaction effects at lower damage levels. A graphical user interface has been developed to predict fragility curves based on various inputs to promote the practical use of machine learning in engineering. Two new 4-story frames are used as case studies, subjected to unseen ground motions to assess the application of trained machine learning algorithms. Prediction errors in input-output scenarios considering soil-structure interaction range from 3% to 18% for new frames. The proposed approach for predicting EDPs is further acknowledged by evaluating a real instrumented five-story steel frame office building.

本研究提出了一种基于非模型的机器学习框架，用于估算具有土-结构相互作用效应的偏心支撑框架的工程需求参数（EDP）。目的是使用传统回归方程和先进的机器学习技术估算残余漂移率和峰值层漂移率、峰值楼层加速度并绘制脆性曲线。通过考虑土壤与结构的相互作用，开发了校正系数以提高预测精度。开发了一个综合数据库，其中包括 4 层和 8 层框架的增量动态分析结果，由 109,841 个数据点组成。数据库包括固定基座模型和具有不同土-结构相互作用值的模型，受 44 种远场地震动影响。考虑到不同的输入变量，引入了四种情景，以比较土壤-结构相互作用的影响。研究结果表明，土-结构相互作用特征对机器学习算法的性能有影响，决定系数最多可增加 17.61%。利用预测的楼层漂移率，两种类型的脆性曲线显示了更精确的预测结果，强调了在较低破坏水平下土层与结构相互作用效应的影响。为了促进机器学习在工程中的实际应用，我们开发了一个图形用户界面，用于根据各种输入预测脆性曲线。使用两个新的 4 层框架作为案例研究，通过未见的地面运动来评估训练有素的机器学习算法的应用情况。在考虑土壤与结构相互作用的输入-输出情景中，新框架的预测误差在 3% 到 18% 之间。通过对一栋真实的五层钢结构办公楼进行评估，我们进一步确认了所提出的 EDP 预测方法。

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引用次数: 0

A lightweight and explainable model for driver abnormal behavior recognition 用于识别驾驶员异常行为的轻量级可解释模型

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-10-31 DOI: 10.1016/j.engappai.2024.109559

With the advancement of intelligent transportation systems, accurate identification of driver abnormal behavior is crucial for enhancing road safety. However, the limited computing power of vehicular systems poses a challenge for running efficient and explainable behavior recognition models. This paper proposes a lightweight and explainable driver abnormal behavior recognition model based on an improved You Only Look Once version 8 (YOLOv8). Firstly, a Spatial and Channel Reconstruction Convolution (SCConv) module is introduced to optimize the Convolution to Feature (C2f) structure, enhancing the model's feature extraction capabilities while reducing parameter redundancy. Secondly, a Spatial Pyramid Pooling with Fast Large Separable Kernel Attention (SPPF-LSKA) module is designed to better capture image context and integrate global information. Additionally, a Dynamic upsample (Dysample) module is introduced to improve the model's ability to capture subtle driver movements. Lastly, a Lightweight Shared Group Normalization Convolution Detection Head (LSGCDH) is designed to enhance the model's generalization ability, significantly reducing the model's computational load, parameter count, and size. Experimental results demonstrate that our approach has significant advantages for edge device deployment compared to mainstream algorithms. The visualization results effectively corroborate the role of each improved structure, enhancing the explainability of the abnormal behavior recognition model, which is beneficial for deployment in vehicular systems and contributes to improving road traffic safety.

随着智能交通系统的发展，准确识别驾驶员的异常行为对提高道路安全至关重要。然而，车辆系统的计算能力有限，这对运行高效且可解释的行为识别模型提出了挑战。本文基于改进的 You Only Look Once version 8（YOLOv8），提出了一种轻量级、可解释的驾驶员异常行为识别模型。首先，引入空间和通道重构卷积（SCConv）模块，优化卷积到特征（C2f）结构，增强模型的特征提取能力，同时减少参数冗余。其次，为了更好地捕捉图像上下文并整合全局信息，设计了空间金字塔池化与快速大型可分离内核关注（SPPF-LSKA）模块。此外，还引入了动态上采样（Dysample）模块，以提高模型捕捉驾驶员细微动作的能力。最后，我们还设计了一个轻量级共享组归一化卷积检测头（LSGCDH），以增强模型的泛化能力，从而显著降低模型的计算负荷、参数数量和大小。实验结果表明，与主流算法相比，我们的方法在边缘设备部署方面具有显著优势。可视化结果有效地证实了每个改进结构的作用，增强了异常行为识别模型的可解释性，有利于在车辆系统中的部署，有助于提高道路交通安全。

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引用次数: 0

Dual-path aggregation transformer network for super-resolution with images occlusions and variability 用于图像遮挡和可变性超分辨率的双路径聚合变换器网络

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-10-31 DOI: 10.1016/j.engappai.2024.109535

While Transformer-based approaches have recently achieved notable success in super-resolution, their extensive computational requirements impede widespread practical adoption. High-resolution meteorological satellite cloud imagery is essential for weather analysis and forecasting. Enhancing image resolution through super-resolution techniques facilitates the accurate identification and localization of geographic features by meteorological systems. However, current super-resolution methods fail to restore the intricacies of cloud formations and complex regions fully. This research introduces a novel dual-path aggregation Transformer network (DPAT) tailored to enhance the super-resolution of meteorological satellite cloud images. The DPAT network adeptly captures cloud imagery's subtle details and textures, effectively addressing occlusions and the variability inherent in satellite imagery. It bolsters the model's ability to manage the complex attributes of cloud images through the introduction of the Dual-path Aggregation Self-Attention (DASA) mechanism and the Multi-scale Feature Aggregation Block (MFAB), thereby enhancing performance in processing intricate cloud features. The DASA mechanism synthesizes features across spatial, depth, and channel dimensions via a dual-path approach, thoroughly exploiting feature correlations. The MFAB, designed to supplant the multilayer perceptron, incorporates shift convolution and a multi-scale interaction block to augment feature information, compensating for the deficiency in local information absorption due to fixed receptive fields. Experimental outcomes indicate that DPAT delivers superior super-resolution outcomes. With a parameter count of only 32% of the Enhanced Deep Residual Network (EDSR) or 77% of the Image Restoration using Shift Window Transformer (SwinIR), DPAT matches SwinIR's performance on the satellite cloud dataset. Moreover, DPAT balances accuracy and parameter economy across various datasets. This technology is expected to improve image super-resolution capabilities in multiple fields such as human action recognition and industrial recognition, and indirectly improve the accuracy of image perception tasks.

虽然基于变压器的方法最近在超分辨率方面取得了显著成功，但其庞大的计算需求阻碍了其广泛的实际应用。高分辨率气象卫星云图对于天气分析和预报至关重要。通过超分辨率技术提高图像分辨率有助于气象系统准确识别和定位地理特征。然而，目前的超分辨率方法无法完全还原错综复杂的云层和复杂区域。本研究介绍了一种新颖的双路径聚合变压器网络（DPAT），旨在增强气象卫星云图的超分辨率。DPAT 网络能够巧妙地捕捉云图像的微妙细节和纹理，有效地解决遮挡和卫星图像固有的多变性问题。它通过引入双路径聚合自注意（DASA）机制和多尺度特征聚合块（MFAB），增强了模型管理云图像复杂属性的能力，从而提高了处理复杂云特征的性能。DASA 机制通过双路径方法合成跨空间、深度和信道维度的特征，充分利用特征相关性。MFAB 是为取代多层感知器而设计的，它结合了移位卷积和多尺度交互块来增强特征信息，弥补了固定感受野导致的局部信息吸收不足。实验结果表明，DPAT 能提供卓越的超分辨率结果。DPAT 的参数数量仅为增强型深度残差网络（EDSR）的 32%，或使用移位窗变换器进行图像复原（SwinIR）的 77%，在卫星云数据集上与 SwinIR 的性能不相上下。此外，DPAT 还能在各种数据集上平衡精度和参数经济性。这项技术有望在人类动作识别和工业识别等多个领域提高图像超分辨率能力，并间接提高图像感知任务的准确性。

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引用次数: 0

A pre-trained multi-step prediction informer for ship motion prediction with a mechanism-data dual-driven framework 采用机制-数据双驱动框架的预训练多步骤船舶运动预测信息器

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-10-30 DOI: 10.1016/j.engappai.2024.109523

The advancement of autonomous maritime surface ships has increased the need for accurate and rapid multi-step prediction of ship motion for decision-making, motion planning, and real-time control tasks. This paper proposes a multi-step prediction method based on Informer with a pre-trained strategy to achieve accurate and fast motion prediction for ships, which substitutes generative inference for rolling prediction to avoid the cumulative error caused by the increasing time horizon. Due to the difference in temporal features from long-term control actions and short-term state sequences, heterogeneous inputs of encoder and decoder are designed to respectively capture their information without information redundancy. To address the bottleneck between the high cost of real data acquisition and the high demand for deep learning methods for data, we propose a mechanism-data dual-driven framework. This framework utilizes a prior mechanism model to generate virtual data incorporating a range of excitation signals designed in accordance with the results of free-running model tests. To reduce the need for real data and increase interpretability, the improved Informer is pre-trained by virtual data from the mechanism model before being trained by real data. Our experiments for multi-step ship motion prediction demonstrate that the proposed method respectively reduces the error and time to 41.36% and 13.20% on average compared to state-of-the-art and classical methods.

随着自主海上水面舰艇的发展，决策、运动规划和实时控制任务更加需要精确、快速的多步骤船舶运动预测。本文提出了一种基于 Informer 的多步预测方法，采用预训练策略实现精确、快速的船舶运动预测，该方法以生成推理代替滚动预测，避免了因时间跨度增大而产生的累积误差。由于长期控制行动和短期状态序列的时间特征不同，编码器和解码器的异构输入设计分别捕获它们的信息，而不会出现信息冗余。为解决真实数据获取成本高和深度学习方法对数据需求大之间的瓶颈，我们提出了机制-数据双驱动框架。该框架利用先验机制模型生成虚拟数据，其中包含根据自由运行模型测试结果设计的一系列激励信号。为了减少对真实数据的需求并提高可解释性，改进后的 Informer 在使用真实数据进行训练之前，先使用来自机构模型的虚拟数据进行预训练。我们对多步骤船舶运动预测的实验表明，与最先进的方法和经典方法相比，所提出的方法平均误差和时间分别减少了 41.36% 和 13.20%。

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引用次数: 0

A novel ensemble method based on residual convolutional neural network with attention module for transient stability assessment considering operational variability 基于带关注模块的残差卷积神经网络的新型集合方法，用于考虑运行变异性的瞬态稳定性评估

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-10-30 DOI: 10.1016/j.engappai.2024.109519

Data-driven methods have been extensively applied in the field of power system transient stability assessment (TSA) owing to their robust capabilities to excavate valuable features. However, TSA methods still face significant challenges in predictive accuracy and generalization ability under variable operation conditions with fluctuating loads or power generations. To address this, a data-driven ensemble TSA method which integrates convolutional block attention module (CBAM) with residual network (ResNet) is proposed to enhance the prediction accuracy. Meanwhile, the traditional cross entropy loss function is replaced by the focal loss function, aiming to reduce the misclassification of unstable samples. Moreover, a rapid updating strategy integrating active learning and fine turning techniques is suggested. It can renew the classifier quickly with limited labeled samples and less time when the network topology changes substantially and makes the pre-trained TSA model unavailable, thus ensuring optimal performance on the new topology. Finally, case studies conducted on the New England 10-machine 39-bus system and the Western Electricity Coordinating Council (WECC) 29-machine 179-bus system validate the effectiveness and robustness of the proposed TSA method. The accuracy of the proposed TSA method achieves 99.56% on 10-machine system and 99.47% on 29-machine system separately, demonstrating the superiority of the proposed TSA method.

由于数据驱动方法具有挖掘有价值特征的强大能力，因此已被广泛应用于电力系统暂态稳定性评估（TSA）领域。然而，在负载或发电量波动的多变运行条件下，TSA 方法在预测精度和泛化能力方面仍面临巨大挑战。针对这一问题，我们提出了一种数据驱动的集合 TSA 方法，该方法将卷积块注意模块（CBAM）与残差网络（ResNet）集成在一起，以提高预测精度。同时，用焦点损失函数取代了传统的交叉熵损失函数，以减少对不稳定样本的误分类。此外，还提出了一种融合了主动学习和微调技术的快速更新策略。当网络拓扑结构发生重大变化，导致预先训练好的 TSA 模型无法使用时，它可以在有限的标注样本下快速更新分类器，从而确保在新的拓扑结构下获得最佳性能。最后，在新英格兰 10 台机器 39 总线系统和西部电力协调委员会（WECC）29 台机器 179 总线系统上进行的案例研究验证了所提出的 TSA 方法的有效性和鲁棒性。建议的 TSA 方法在 10 机系统和 29 机系统上的准确率分别达到 99.56% 和 99.47%，证明了建议的 TSA 方法的优越性。

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引用次数: 0

Hybrid neural network-based fractional-order sliding mode controller for tracking control problem of reconfigurable robot manipulators using fast terminal type switching law 基于混合神经网络的分数阶滑动模式控制器，利用快速终端型切换定律解决可重构机器人机械手的跟踪控制问题

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-10-30 DOI: 10.1016/j.engappai.2024.109515

A hybrid neural network-based fractional-order sliding mode controller for the position/force tracking control problem of a reconfigurable robot manipulator system is presented in this work. Due to interchangeable link modules, modeling uncertainties, coupled interconnected states, etc., the control of reconfigurable robot manipulators is very complicated in variable circumstances. So, to handle these dynamical systems, initially, a stable fractional-order sliding manifold is introduced to facilitate accurate and faster system state responses. Subsequently, a neural network-based fractional-order fast terminal sliding mode controller is designed to manage the consequences of external disruptions and parametric uncertainties effectively. In the controller’s design, a hybrid combination of radial basis function neural network and adaptive compensator with fast terminal type switching law is opted for robust performance of the dynamical system. The novelty of the work lies in the combination of the hybrid intelligent sliding mode control scheme with fractional calculus for tracking control problems of reconfigurable robot manipulator systems. The proposed scheme improves the transient response of the controller with a fast terminal-type switching law, and addresses the robustness, fixed-time convergence of system states along with an explicit assessment of the settling time. Finally, the asymptotic stability of the closed-loop dynamical system is validated through Lyapunov’s stability criteria and Fractional-order Barbalat’s lemma, and the simulation results along with a comparative study with some quantitative statistical evaluations confirm the contribution of the presented work.

本研究针对可重构机器人机械手系统的位置/力跟踪控制问题，提出了一种基于混合神经网络的分数阶滑动模式控制器。由于可互换链接模块、建模不确定性、耦合互连状态等原因，可重构机器人机械手的控制在多变情况下非常复杂。因此，为了处理这些动态系统，最初引入了一个稳定的分数阶滑动流形，以促进准确、快速的系统状态响应。随后，设计了基于神经网络的分数阶快速终端滑模控制器，以有效控制外部干扰和参数不确定性的后果。在控制器的设计中，选择了径向基函数神经网络和自适应补偿器与快速终端型开关规律的混合组合，以实现动态系统的稳健性能。这项工作的新颖之处在于将混合智能滑模控制方案与分数微积分相结合，以解决可重构机器人机械手系统的跟踪控制问题。所提出的方案利用快速终端型切换法则改善了控制器的瞬态响应，并解决了系统状态的鲁棒性和固定时间收敛问题，同时还明确评估了沉降时间。最后，通过 Lyapunov 稳定性准则和分数阶 Barbalat Lemma 验证了闭环动态系统的渐进稳定性，仿真结果以及与一些定量统计评估的比较研究证实了本文的贡献。

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引用次数: 0