Journal of The Franklin Institute-engineering and Applied Mathematics最新文献

英文中文

Distributed adaptive event-triggered finite-time fault-tolerant containment control for multi-UAVs with input constraints and actuator failures 针对具有输入约束和执行器故障的多无人飞行器的分布式自适应事件触发有限时间容错遏制控制

IF 3.7 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-29 DOI: 10.1016/j.jfranklin.2024.107308

This article investigates the distributed adaptive finite-time containment control problem for multi-UAVs with input constraints, actuator failures, communication limitations, and external disturbances. First, a new smoothing function is used to smooth the asymmetric input constraint signals so that the input constraint and actuator fault control problem can be transformed into a variable gain control problem. Subsequently, a new Nussbaum function is proposed to solve the variable gain control problem. A new adaptive event-triggered strategy is designed to solve the communication limitation problem, and the trigger threshold has the characteristic of adaptive adjustment that can be dynamically decreased. In response to external disturbances, an adaptive law is designed to estimate and compensate for the boundaries of disturbances. It follows from the analysis based on Lyapunov theory that under the proposed controller, the followers will converge to the convex envelope formed by the leaders in a finite time, and Zeno-free is achieved. Simulation results are provided to verify the effectiveness of the developed adaptive event-triggered finite-time fault-tolerant containment control laws.

本文研究了具有输入约束、致动器故障、通信限制和外部干扰的多无人机分布式自适应有限时间遏制控制问题。首先，使用一种新的平滑函数来平滑非对称输入约束信号，从而将输入约束和致动器故障控制问题转化为可变增益控制问题。随后，提出了一种新的 Nussbaum 函数来解决可变增益控制问题。设计了一种新的自适应事件触发策略来解决通信限制问题，触发阈值具有可动态降低的自适应调整特性。针对外部干扰，设计了自适应法则来估计和补偿干扰的边界。通过基于 Lyapunov 理论的分析可知，在所提出的控制器下，跟随者将在有限的时间内收敛到领导者所形成的凸包络中，实现了无 Zeno。仿真结果验证了所开发的自适应事件触发有限时间容错遏制控制法的有效性。

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引用次数: 0

Extended state observer-based finite-time trajectory tracking control for wheeled mobile robots under FDI attacks FDI 攻击下基于扩展状态观测器的轮式移动机器人有限时间轨迹跟踪控制

IF 3.7 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-28 DOI: 10.1016/j.jfranklin.2024.107304

The trajectory tracking control of wheeled mobile robots (WMRs) under False Data Injection (FDI) attacks is investigated in this paper. First, to mitigate the impact of FDI attacks, a finite-time extended state observer (FTESO) is constructed. The error dynamic of the tracking control system is then split up into two subsystems via the cascaded control approach, and a one-to-one finite-time control law is designed for both subsystems. Second, a new stability condition is proposed to ensure that the tracking error system is finite-time globally uniform ultimate bounded (GUUB) and the tracking error can converge into a compact set. Finally, it is validated that the proposed control method is effective through simulation studies.

本文研究了轮式移动机器人（WMR）在虚假数据注入（FDI）攻击下的轨迹跟踪控制。首先，为了减轻 FDI 攻击的影响，构建了一个有限时间扩展状态观测器（FTESO）。然后，通过级联控制方法将跟踪控制系统的误差动态分成两个子系统，并为这两个子系统设计了一对一的有限时间控制律。其次，提出了一个新的稳定性条件，以确保跟踪误差系统是有限时间全局均匀终极有界（GUUB）的，并且跟踪误差能收敛到一个紧凑集。最后，通过仿真研究验证了所提出的控制方法是有效的。

引用次数: 0

A zonotope-based fault detection method for switched systems with parameter uncertainties and multiple time delays 针对参数不确定和多时间延迟的开关系统的基于区域顶的故障检测方法

IF 3.7 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-28 DOI: 10.1016/j.jfranklin.2024.107305

This paper studies fault detection for discrete-time switched systems with parameter uncertainties and time delays. First, a state augmentation method is presented to decouple the effect of time delays. Then, a novel residual generator structure based on priori state estimation is introduced, and the adaptive thresholds for residual evaluation are computed via zonotope techniques. Meanwhile, peak-to-peak performance and

H_{-}

performance are adopted to design the residual generator, ensuring both uncertainty robustness and fault sensitivity. Finally, numerical simulation results are provided to demonstrate the effectiveness and superiority of the proposed method.

本文研究了具有参数不确定性和时间延迟的离散时间开关系统的故障检测。首先，本文提出了一种状态增强方法，以消除时间延迟的影响。然后，介绍了一种基于先验状态估计的新型残差发生器结构，并通过区角技术计算了残差评估的自适应阈值。同时，采用峰-峰性能和 H-性能设计残差发生器，确保了不确定性鲁棒性和故障灵敏度。最后，还提供了数值模拟结果，以证明所提方法的有效性和优越性。

引用次数: 0

MIMO Super-Twisting Controller using a passivity-based design 采用基于被动性设计的 MIMO 超扭曲控制器

IF 3.7 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-28 DOI: 10.1016/j.jfranklin.2024.107296

A novel MIMO Super-Twisting Algorithm is proposed in this paper for nonlinear systems with relative degree one, having a time and state-varying uncertain control matrix. The uncertainty is represented by a time and state-varying but unknown left matrix factor. Sufficient conditions for stability with full-matrix control gains are established, in contrast to the usual scalar gains. For this a smooth Lyapunov function, based on a passivity interpretation, is used. Moreover, continuous and homogeneous approximations of the classical discontinuous Super-Twisting algorithm are obtained, using a unified analysis method. Moreover, the proposed Super-Twisting algorithm is of the discontinuous type, in contrast to the usual unitary type.

本文提出了一种新颖的 MIMO 超级扭转算法，适用于具有时变和状态变量不确定控制矩阵的相对阶数为 1 的非线性系统。不确定性由时间和状态变化但未知的左矩阵因子表示。与通常的标量增益不同，本文建立了全矩阵控制增益稳定性的充分条件。为此，使用了基于被动性解释的平滑 Lyapunov 函数。此外，利用统一的分析方法，还获得了经典不连续超级扭曲算法的连续和同质近似值。此外，所提出的超级扭转算法是非连续型的，而不是通常的单元型。

引用次数: 0

Consensus formation control of wheeled mobile robots with mixed disturbances under input constraints 输入约束条件下具有混合干扰的轮式移动机器人的共识形成控制

IF 3.7 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-27 DOI: 10.1016/j.jfranklin.2024.107300

This paper addresses the problem of distributed consensus-based formation control for wheeled mobile robots (WMRs) under the influence of mixed disturbances, including both random noise and non-random disturbances. A consensus formation auxiliary subsystem is constructed based on the leader’s position estimated by the distributed estimator. A formation tracking subsystem for each robot is constructed based on the trajectory tracking error method. The above two subsystems are constructed into an extended formation modeling system. Further, a distributed model predictive control (DMPC) is designed to control this system without disturbance, and the controller is solved by means of a general-purpose neural network. A combination of Kalman filter (KF) and extended state observer (ESO) is intended to reduce the effect of both non-random disturbances and random noise, hence increasing the controller’s resilience to disturbances. Moreover, a composite control law is designed to ensure the controller’s effectiveness. Finally, simulation results demonstrate that the proposed control strategy is well-suited to addressing the problem, as it not only achieves accurate formation control but also effectively regulates the robot’s physical constraints while suppressing both non-random disturbances and random noise.

本文探讨了在混合干扰（包括随机噪声和非随机干扰）影响下，轮式移动机器人（WMR）基于分布式共识的编队控制问题。根据分布式估算器估算出的领导者位置，构建了一个共识编队辅助子系统。基于轨迹跟踪误差法，为每个机器人构建编队跟踪子系统。上述两个子系统被构建成一个扩展的编队建模系统。此外，还设计了分布式模型预测控制（DMPC）来控制该系统不受干扰，该控制器通过通用神经网络求解。卡尔曼滤波器（KF）和扩展状态观测器（ESO）的组合旨在减少非随机干扰和随机噪声的影响，从而提高控制器的抗干扰能力。此外，还设计了一种复合控制法则，以确保控制器的有效性。最后，仿真结果表明，所提出的控制策略非常适合解决这一问题，因为它不仅能实现精确的编队控制，还能有效调节机器人的物理约束，同时抑制非随机干扰和随机噪声。

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引用次数: 0

Fuzzy adaptive fixed-time control for output-constrained uncertain nonstrict-feedback time-delay systems 输出受限不确定非严格反馈时延系统的模糊自适应固定时间控制

IF 3.7 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-27 DOI: 10.1016/j.jfranklin.2024.107302

This paper deals with the problem of fuzzy adaptive fixed-time control for a class of uncertain nonlinear nonstrict feedback systems with time delays and output constraints. The negative effects of time delays on system performance and stability are overcome by introducing a new Barrier Lyapunov–Krasovskii (BLK) function and an innovative inequality. In addition, the singularity problem that arises in the process of deriving the control and adaptive laws, is solved by the proposed BLK function. The controller is designed in the backstepping framework which ensures that the system output does not violate predefined constraints. The proposed controller needs less information from the system and therefore has less implementation complexity than previous references, which leads to a more straightforward implementation. A fuzzy system approximation is utilized to overcome the uncertainties of the model. It is guaranteed that all closed-loop system signals converge to small neighborhoods around zero in a fixed time. Finally, an example is presented to verify that the proposed method achieves the control objectives.

本文探讨了一类具有时间延迟和输出约束的不确定非线性非严格反馈系统的模糊自适应固定时间控制问题。通过引入新的障碍李亚普诺夫-克拉索夫斯基（BLK）函数和创新的不等式，克服了时间延迟对系统性能和稳定性的负面影响。此外，在推导控制和自适应定律的过程中出现的奇异性问题，也通过所提出的 BLK 函数得到了解决。控制器是在后步法框架下设计的，可确保系统输出不违反预定义的约束条件。与之前的参考文献相比，所提出的控制器需要的系统信息更少，因此执行复杂度更低，从而实现了更直接的执行。利用模糊系统近似来克服模型的不确定性。保证所有闭环系统信号都能在固定时间内收敛到零点附近的小邻域。最后，介绍了一个实例来验证所提出的方法是否实现了控制目标。

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引用次数: 0

Face recognition method based on fusion of improved MobileFaceNet and adaptive Gamma algorithm 基于改进型 MobileFaceNet 和自适应伽马算法融合的人脸识别方法

IF 3.7 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-27 DOI: 10.1016/j.jfranklin.2024.107306

MobileFaceNet face recognition algorithm is a relatively mainstream face recognition algorithm at present. Its advantages of small memory and fast running speed make it widely used in embedded devices. Due to the limited face image acquisition capability of embedded devices, the accuracy of face recognition is often reduced due to uneven illumination and poor exposure quality. In order to solve this problem, a face recognition algorithm based on the fusion of MobileFaceNet and adaptive Gamma algorithm is proposed. The application of the algorithm proposed in this paper in image preprocessing is as follows. Firstly, adaptive Gamma correction is used to improve the brightness of the face image. Then, the edge of the face image is enhanced by the Laplace operator. Finally, a linear weighted fusion was performed between the Gamma corrected image and the enhanced edge image to obtain the pre-processed face image. At the same time, we have improved the traditional MobileFaceNet network. The feature extraction network MobileFaceNet has been improved by adding a Stylebased Recall Module (SRM) attention mechanism to its bottom neck layer, utilizing the mean and standard deviation of input features to improve the ability to capture global information and enhance more important feature information. Finally, the proposed method was verified on the LFW and Agedb face test set. The experimental results show that the adaptive Gamma algorithm proposed in this paper and the improvement of MobileFaceNet can achieve a face recognition accuracy of 99.27 % on LFW dataset and 90.18 % on Agedb dataset while only increasing the model size by 0.4 M and the processing speed for each image is enhanced by 4 ms. which can effectively improve the accuracy of face recognition and better application prospects on embedded devices. The method presented in this article has certain practical significance.

MobileFaceNet 人脸识别算法是目前比较主流的人脸识别算法。其内存小、运行速度快等优点使其在嵌入式设备中得到广泛应用。由于嵌入式设备的人脸图像采集能力有限，经常会出现光照不均匀、曝光质量差等问题，导致人脸识别的准确率降低。为了解决这一问题，本文提出了一种基于 MobileFaceNet 和自适应伽马算法融合的人脸识别算法。本文提出的算法在图像预处理中的应用如下。首先，使用自适应伽马校正来提高人脸图像的亮度。然后，利用拉普拉斯算子增强人脸图像的边缘。最后，在伽马校正图像和增强的边缘图像之间进行线性加权融合，得到预处理后的人脸图像。与此同时，我们还改进了传统的 MobileFaceNet 网络。我们改进了特征提取网络 MobileFaceNet，在其颈部底层增加了基于风格的召回模块（SRM）关注机制，利用输入特征的平均值和标准差来提高捕捉全局信息的能力，并增强更重要的特征信息。最后，在 LFW 和 Agedb 人脸测试集上对所提出的方法进行了验证。实验结果表明，本文提出的自适应伽马算法和对 MobileFaceNet 的改进，在模型大小仅增加 0.4 M，每幅图像处理速度提高 4 ms 的情况下，在 LFW 数据集上的人脸识别准确率达到 99.27%，在 Agedb 数据集上的人脸识别准确率达到 90.18%，能有效提高人脸识别的准确率，在嵌入式设备上有更好的应用前景。本文提出的方法具有一定的现实意义。

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引用次数: 0

Two improved generalized extended stochastic gradient algorithms for CARARMA systems CARARMA 系统的两种改进型广义扩展随机梯度算法

IF 3.7 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-26 DOI: 10.1016/j.jfranklin.2024.107295

The paper innovatively proposes two improved generalized extended stochastic gradient (GESG) algorithms for the controlled autoregressive autoregressive moving average (CARARMA) system with autoregressive moving average (ARMA) model noise. Firstly, we propose a latest estimation based weighted generalized extended stochastic gradient (LE-WGESG) algorithm, which introduces multiple momentary corrections in the traditional parameter estimation process. By carefully adjusting the weighting coefficients of the correction quantities at different moments, the algorithm has a rapid and greater efficient convergence property. More importantly, utilizing the theory of moving data window, this paper also proposes a multi-innovation based latest estimated weighted generalized extended stochastic gradient (MI-LE-WGESG) algorithm, which can better capture the interactions among multiple correction terms and further improve the predictive ability of the model.

本文针对具有自回归移动平均（ARMA）模型噪声的受控自回归自回归移动平均（CARARMA）系统，创新性地提出了两种改进的广义扩展随机梯度（GESG）算法。首先，我们提出了一种基于最新估计的加权广义扩展随机梯度（LE-WGESG）算法，该算法在传统参数估计过程中引入了多个时刻修正。通过仔细调整修正量在不同时刻的加权系数，该算法具有快速和更高效的收敛特性。更重要的是，本文还利用移动数据窗理论，提出了基于多创新的最新估计加权广义扩展随机梯度（MI-LE-WGESG）算法，该算法能更好地捕捉多个修正项之间的相互作用，进一步提高模型的预测能力。

引用次数: 0

Spatial–Temporal Similarity Fusion Graph Adversarial Convolutional Networks for traffic flow forecasting 用于交通流量预测的时空相似性融合图对抗卷积网络

IF 3.7 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-26 DOI: 10.1016/j.jfranklin.2024.107299

Traffic flow forecasting is integral to the advancement of intelligent transportation systems and the development of smart cities. This paper introduces a novel model, the Spatial–Temporal Similarity Fusion Graphs Adversarial Convolutional Networks (STSF-GACN), which leverages advanced data preprocessing techniques to enhance the predictive accuracy and efficiency of traffic flow forecasting.

The innovation of our approach lies in the meticulous construction of the spatial–temporal similarity matrix through the precise calculation of temporal and spatial similarities. This matrix forms the backbone of our model, serving as the generator in the integrated Generative Adversarial Network (GAN) architecture. The Spatial–Temporal Similarity Fusion Adaptive Graph Convolutional Network, developed as part of our GAN’s generator, utilizes cutting-edge techniques such as the Wasserstein distance and Dynamic Time Warping to optimize the adaptive adjacency matrix, enabling the model to capture latent spatial–temporal correlations with unprecedented depth and precision.

The discriminator of the GAN further refines the model by evaluating the accuracy of the traffic predictions, ensuring that the generative model produces results that are not only accurate but also robust against varying traffic conditions. This cohesive integration of GAN into the model architecture allows for a significant improvement in prediction accuracy and convergence speed, moving beyond traditional forecasting methods.

交通流量预测是智能交通系统和智慧城市发展不可或缺的一部分。本文介绍了一种新型模型--时空相似性融合图对抗卷积网络（STSF-GACN），它利用先进的数据预处理技术提高了交通流量预测的准确性和效率。该矩阵构成了我们模型的主干，是集成生成对抗网络（GAN）架构中的生成器。时空相似性融合自适应图卷积网络是作为 GAN 生成器的一部分而开发的，它利用瓦瑟斯坦距离和动态时间扭曲等尖端技术来优化自适应邻接矩阵，使模型能够以前所未有的深度和精度捕捉潜在的时空相关性。这种将 GAN 与模型架构紧密结合的方法大大提高了预测精度和收敛速度，超越了传统的预测方法。

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引用次数: 0

A Lyapunov–Razumikhin control strategy for stochastic nonlinear delayed systems with polynomial conditions 具有多项式条件的随机非线性延迟系统的 Lyapunov-Razumikhin 控制策略

IF 3.7 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-26 DOI: 10.1016/j.jfranklin.2024.107288

Numerous control schemes for stochastic systems involving time-varying delays need a strict slow time-delay condition, which does hold for some practical models. Also, some stochastic systems will have different structures as working conditions change. In this article, we will investigate the control of stochastic systems which contain time-varying delays and polynomial conditions. By providing the dynamic-gain based homogeneous domination approach, and selecting a new Lyapunov–Razumikhin (L–R) function, a universal dynamic controller for systems with different growing conditions is presented, and the slow time-delay condition is removed successfully. The algorithm is finally verified with a practical example.

涉及时变延迟的随机系统的许多控制方案都需要严格的慢速时变延迟条件，这在某些实际模型中确实成立。此外，一些随机系统会随着工作条件的变化而产生不同的结构。本文将研究包含时变延迟和多项式条件的随机系统的控制问题。通过提供基于动态增益的同质支配方法，并选择一种新的 Lyapunov-Razumikhin (L-R) 函数，提出了一种适用于不同生长条件系统的通用动态控制器，并成功地消除了慢时延条件。最后通过一个实际例子对该算法进行了验证。

引用次数: 0

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