Multiple Time-Varying Formation of Networked Heterogeneous Robotic Systems via Estimator-Based Hierarchical Cooperative Algorithms

Ge, Ming‐Feng; Liang, Chang‐Duo; Zhan, Xisheng; Chen, Chaoyang; Xu, Ge Ning; Chen, Jie

doi:10.1155/2020/8357428

Cited by 4 publications

(9 citation statements)

References 39 publications

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“…First of all, assume that the stored packet is y i ASS (k) for the ith subsystem at the current sampling time k. Depending on the role of the ASS, the received valid packet is z i (k − τ(k 1 )) with delay τ(k 1 ) before transmission. From equations (2) and (5), the reorganized measurement is denoted as…”

Section: Reorganized Filter and Prediction Vector Of The Statementioning

confidence: 99%

“…Case 1: according to the principle of the spatial positioning system, every point on the z � 100 mm plane, and the range of 81 × 81 mm 2 , the values are obtained on the spatial plane, and the points perceived by the sensor are the spatial points in the three CCD measurable ranges. As shown in Figure 4, the red area represents the measurable point, and the cyan area represents the undetectable point.…”

Section: Measurable Range On the Planementioning

confidence: 99%

“…For the complex and large-scale systems in the field of energy, transportation, logistics, and so on, due to the emerging problems such as strongly nonlinear and highly coupling, they significantly challenge the current computational tools. To solve science and engineering problems based on complex biological mechanisms and living phenomena, bio-inspired computing (BIC) method is used to design and develop the computer algorithms and models, such as neural networks [1], genetic algorithms [2], and artificial immune systems [3].…”

Section: Introductionmentioning

confidence: 99%

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Distributed State Estimation for Dynamic Positioning Systems with Uncertain Disturbances and Transmission Time Delays

Liu

Yao

et al. 2020

Complexity

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The dynamic positioning system of unmanned underwater vehicles (UUVs) is a complex and large-scale system mainly due to the nonlinear dynamics, uncertainty in model parameters, and external disturbances. With the aid of the bio-inspired computing (BIC) method, the designed three-dimensional (3D) spatial positioning system is used for enlarging communication constraints and increasing signal coordination processing. With the growing of measurement scales, the issue of the networked high-precision positioning has been developed rapidly. Then, an information fusion estimation approach is presented for the distributed networked systems with data random transmission time delays and lost and disordered packets. To reduce the communication burden, an adaptive signal selection scheme is employed to reorganize the measurement sequence, and the parameter uncertainties as well as cross-correlated noise are used to describe the uncertain disturbances. Moreover, a reoptimal weighted fusion state estimation is designed to alleviate the information redundancy and maintain higher measurement accuracy. An illustrative example obtained from the 3D spatial positioning system is given to validate the effectiveness of the proposed method.

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Section: Reorganized Filter and Prediction Vector Of The Statementioning

confidence: 99%

Section: Measurable Range On the Planementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distributed State Estimation for Dynamic Positioning Systems with Uncertain Disturbances and Transmission Time Delays

Liu

Yao

et al. 2020

Complexity

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“…The field of formation control application is broad, which has led to its rapid development [6][7][8]. Formation control can be classified into two types of problems [9] based on whether the formation spatial position changes over time: (1) formation problem, which controls multiple agents to achieve a pre-specified formation that does not change over time, and (2) formation tracking problem, which requires agents to track a pre-specified formation trajectory that may change over time.…”

Section: Introductionmentioning

confidence: 99%

Algebraic-Connectivity-Based Multi-USV Distributed Formation Method via Adding a Reverse Edge

et al. 2023

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This paper concerns the formation problem in multi-USV cluster formation containment tracking tasks with a special topology. A topology reconstruction method was proposed that enables the followers’ formation to be dispersed while achieving the fastest convergence rate for the system. This topology structure is based on tree topology and DAG (directed acyclic graph) local structure stem as prototypes, using the principle of adding reverse edges on the stem to reduce algebraic connectivity. By adding a reverse edge to obtain a more dispersed formation, a method for selecting appropriate reverse edges was achieved. Through relevant theoretical quantitative and qualitative analysis, it was demonstrated that adding this reverse edge can enable the system to achieve the fastest convergence rate. Finally, through simulation experiments, it was verified that the selected reverse edge can optimize the formation of followers and achieve the fastest convergence rate.

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“…Text classification refers to the division of texts in the corpus into predefined categories based on its contents and other attributes. It is widely used in many applications [3][4][5][6][7][8][9], such as spam filtering, news categorization, sentiment analysis, and digital library.…”

Section: Introductionmentioning

confidence: 99%

Joint Character-Level Convolutional and Generative Adversarial Networks for Text Classification

et al. 2020

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With the continuous renewal of text classification rules, text classifiers need more powerful generalization ability to process the datasets with new text categories or small training samples. In this paper, we propose a text classification framework under insufficient training sample conditions. In the framework, we first quantify the texts by a character-level convolutional neural network and input the textual features into an adversarial network and a classifier, respectively. Then, we use the real textual features to train a generator and a discriminator so as to make the distribution of generated data consistent with that of real data. Finally, the classifier is cooperatively trained by real data and generated data. Extensive experimental validation on four public datasets demonstrates that our method significantly performs better than the comparative methods.

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Multiple Time-Varying Formation of Networked Heterogeneous Robotic Systems via Estimator-Based Hierarchical Cooperative Algorithms

Cited by 4 publications

References 39 publications

Distributed State Estimation for Dynamic Positioning Systems with Uncertain Disturbances and Transmission Time Delays

Distributed State Estimation for Dynamic Positioning Systems with Uncertain Disturbances and Transmission Time Delays

Algebraic-Connectivity-Based Multi-USV Distributed Formation Method via Adding a Reverse Edge

Joint Character-Level Convolutional and Generative Adversarial Networks for Text Classification

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