A three-dimensional self-adaptive region fuzzy guidance law based on RBF neural networks

AbstractThis article introduces a method for realizing the Gaussian activation function of radial-basis (RBF) neural networks with their hardware implementation on field-programmable gaits area (FPGAs). The results of modeling of the Gaussian function on FPGA chips of different families have been presented. RBF neural networks of various topologies have been synthesized and investigated. The hardware component implemented by this algorithm is an RBF neural network with four neurons of the latent layer and one neuron with a sigmoid activation function on an FPGA using 16-bit numbers with a fixed point, which took 1193 logic matrix gate (LUTs—LookUpTable). Each hidden layer neuron of the RBF network is designed on an FPGA as a separate computing unit. The speed as a total delay of the combination scheme of the block RBF network was 101.579 ns. The implementation of the Gaussian activation functions of the hidden layer of the RBF network occupies 106 LUTs, and the speed of the Gaussian activation functions is 29.33 ns. The absolute error is ± 0.005. The Spartan 3 family of chips for modeling has been used to get these results. Modeling on chips of other series has been also introduced in the article. RBF neural networks of various topologies have been synthesized and investigated. Hardware implementation of RBF neural networks with such speed allows them to be used in real-time control systems for high-speed objects.

Download Full-text

Three-dimensional cooperative guidance strategy and guidance law for intercepting highly maneuvering target

Chinese Journal of Aeronautics ◽

10.1016/j.cja.2020.12.014 ◽

2021 ◽

Vol 34 (5) ◽

pp. 485-495

Author(s):

Ziyan CHEN ◽

Jianglong YU ◽

Xiwang DONG ◽

Zhang REN

Keyword(s):

Three Dimensional ◽

Maneuvering Target ◽

Guidance Law ◽

Cooperative Guidance

Download Full-text

Task-space regulation of rigid-link electrically-driven robots with uncertain kinematics using neural networks

Measurement and Control ◽

10.1177/0020294020983383 ◽

2021 ◽

Vol 54 (1-2) ◽

pp. 102-115

Author(s):

Wenhui Si ◽

Lingyan Zhao ◽

Jianping Wei ◽

Zhiguang Guan

Keyword(s):

Neural Networks ◽

Asymptotic Stability ◽

Control Method ◽

Joint Space ◽

Robot Kinematics ◽

Rbf Neural Networks ◽

Task Space ◽

Actuator Dynamics ◽

Rigid Link ◽

On Line

Extensive research efforts have been made to address the motion control of rigid-link electrically-driven (RLED) robots in literature. However, most existing results were designed in joint space and need to be converted to task space as more and more control tasks are defined in their operational space. In this work, the direct task-space regulation of RLED robots with uncertain kinematics is studied by using neural networks (NN) technique. Radial basis function (RBF) neural networks are used to estimate complicated and calibration heavy robot kinematics and dynamics. The NN weights are updated on-line through two adaptation laws without the necessity of off-line training. Compared with most existing NN-based robot control results, the novelty of the proposed method lies in that asymptotic stability of the overall system can be achieved instead of just uniformly ultimately bounded (UUB) stability. Moreover, the proposed control method can tolerate not only the actuator dynamics uncertainty but also the uncertainty in robot kinematics by adopting an adaptive Jacobian matrix. The asymptotic stability of the overall system is proven rigorously through Lyapunov analysis. Numerical studies have been carried out to verify efficiency of the proposed method.

Download Full-text

Three-Dimensional Fixed-Time Cooperative Guidance Law With Impact Angle Constraint and Prespecified Impact Time

IEEE Access ◽

10.1109/access.2021.3057428 ◽

2021 ◽

Vol 9 ◽

pp. 29755-29763

Author(s):

Mu Lin ◽

Xiangjun Ding ◽

Chunyan Wang ◽

Li Liang ◽

Jianan Wang

Keyword(s):

Three Dimensional ◽

Fixed Time ◽

Impact Angle ◽

Impact Time ◽

Guidance Law ◽

Cooperative Guidance ◽

Impact Angle Constraint

Download Full-text

Generation and Annotation of Simulation-Real Ship Images for Convolutional Neural Networks Training and Testing

Applied Sciences ◽

10.3390/app11135931 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5931

Author(s):

Ji’an You ◽

Zhaozheng Hu ◽

Chao Peng ◽

Zhiqiang Wang

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Image Annotation ◽

Three Dimensional ◽

Image Data ◽

Detection Algorithm ◽

Simulation Software ◽

High Quality ◽

Annotation Method ◽

Detection Of Objects

Large amounts of high-quality image data are the basis and premise of the high accuracy detection of objects in the field of convolutional neural networks (CNN). It is challenging to collect various high-quality ship image data based on the marine environment. A novel method based on CNN is proposed to generate a large number of high-quality ship images to address this. We obtained ship images with different perspectives and different sizes by adjusting the ships’ postures and sizes in three-dimensional (3D) simulation software, then 3D ship data were transformed into 2D ship image according to the principle of pinhole imaging. We selected specific experimental scenes as background images, and the target ships of the 2D ship images were superimposed onto the background images to generate “Simulation–Real” ship images (named SRS images hereafter). Additionally, an image annotation method based on SRS images was designed. Finally, the target detection algorithm based on CNN was used to train and test the generated SRS images. The proposed method is suitable for generating a large number of high-quality ship image samples and annotation data of corresponding ship images quickly to significantly improve the accuracy of ship detection. The annotation method proposed is superior to the annotation methods that label images with the image annotation software of Label-me and Label-img in terms of labeling the SRS images.

Download Full-text

Design of finite-time guidance law based on observer and head-pursuit theory

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410020984562 ◽

2021 ◽

pp. 095441002098456

Author(s):

Chenqi Zhu

Keyword(s):

Finite Time ◽

Three Dimensional ◽

Hypersonic Vehicle ◽

Two Dimensional ◽

Dimensional Model ◽

Reaching Law ◽

Guidance Law ◽

Fast Power ◽

Simulation Results ◽

Guidance Laws

In order to improve the guiding accuracy in intercepting the hypersonic vehicle, this article presents a finite-time guidance law based on the observer and head-pursuit theory. First, based on a two-dimensional model between the interceptor and target, this study applies the fast power reaching law to head-pursuit guidance law so that it can alleviate the chattering phenomenon and ensure the convergence speed. Second, target maneuvers are considered as system disturbances, and the head-pursuit guidance law based on an observer is proposed. Furthermore, this method is extended to a three-dimensional case. Finally, comparative simulation results further verify the superiority of the guidance laws designed in this article.

Download Full-text

Identification of Mode Shapes of a Composite Cylinder Using Convolutional Neural Networks

Materials ◽

10.3390/ma14112801 ◽

2021 ◽

Vol 14 (11) ◽

pp. 2801

Author(s):

Bartosz Miller ◽

Leonard Ziemiański

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Composite Structures ◽

Three Dimensional ◽

Mode Shapes ◽

Identification Algorithm ◽

Material Degradation ◽

Local Material ◽

Noisy Input ◽

Shape Vector

The aim of the following paper is to discuss a newly developed approach for the identification of vibration mode shapes of multilayer composite structures. To overcome the limitations of the approaches based on image analysis (two-dimensional structures, high spatial resolution of mode shapes description), convolutional neural networks (CNNs) are applied to create a three-dimensional mode shapes identification algorithm with a significantly reduced number of mode shape vector coordinates. The CNN-based procedure is accurate, effective, and robust to noisy input data. The appearance of local damage is not an obstacle. The change of the material and the occurrence of local material degradation do not affect the accuracy of the method. Moreover, the application of the proposed identification method allows identifying the material degradation occurrence.

Download Full-text

Decentralized adaptive tracking control for high-order interconnected stochastic nonlinear time-varying delay systems with stochastic input-to-state stable inverse dynamics by neural networks

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331219834611 ◽

2019 ◽

Vol 41 (13) ◽

pp. 3612-3625 ◽

Cited By ~ 1

Author(s):

Wang Qian ◽

Wang Qiangde ◽

Wei Chunling ◽

Zhang Zhengqiang

Keyword(s):

Neural Networks ◽

Tracking Control ◽

Large Scale ◽

Inverse Dynamics ◽

Small Neighborhood ◽

High Order ◽

Interconnected Systems ◽

Delay Systems ◽

Rbf Neural Networks ◽

Stochastic Input

The paper solves the problem of a decentralized adaptive state-feedback neural tracking control for a class of stochastic nonlinear high-order interconnected systems. Under the assumptions that the inverse dynamics of the subsystems are stochastic input-to-state stable (SISS) and for the controller design, Radial basis function (RBF) neural networks (NN) are used to cope with the packaged unknown system dynamics and stochastic uncertainties. Besides, the appropriate Lyapunov-Krosovskii functions and parameters are constructed for a class of large-scale high-order stochastic nonlinear strong interconnected systems with inverse dynamics. It has been proved that the actual controller can be designed so as to guarantee that all the signals in the closed-loop systems remain semi-globally uniformly ultimately bounded, and the tracking errors eventually converge in the small neighborhood of origin. Simulation example has been proposed to show the effectiveness of our results.

Download Full-text