Predictive high-order variable structure filter

2021 ◽  
pp. 211-231
Author(s):  
Lu Cao ◽  
Xiaoqian Chen ◽  
Bing Xiao
Keyword(s):  
Variable Structure ◽  
High Order ◽  
Order Variable

scholarly journals An SVM-Based Neural Adaptive Variable Structure Observer for Fault Diagnosis and Fault-Tolerant Control of a Robot Manipulator

2020 ◽  
Vol 10 (4) ◽  
pp. 1344 ◽  
Author(s):  
Farzin Piltan ◽  
Alexander E. Prosvirin ◽  
Muhammad Sohaib ◽  
Belem Saldivar ◽  
Jong-Myon Kim
Keyword(s):  
Fault Diagnosis ◽  
Fault Tolerant ◽  
Robot Manipulator ◽  
Variable Structure ◽  
Fault Tolerant Control ◽  
High Order ◽  
Support Vector ◽  
Structure Control ◽  
Hybrid Controller ◽  
Order Variable

A robot manipulator is a multi-degree-of-freedom and nonlinear system that is used in various applications, including the medical area and automotive industries. Uncertain conditions in which a robot manipulator operates, as well as its nonlinearities, represent challenges for fault diagnosis and fault-tolerant control (FDC) that are addressed through the proposed FDC technique. A machine-learning-based neural adaptive, high-order, variable structure observer for fault diagnosis (FD) and adaptive, modern, fuzzy, backstepping, variable structure control for use in a fault-tolerant control (FC) algorithm, are proposed in this paper. In the first stage, a variable structure observer is proposed as an FD technique for the robot manipulator. The chattering phenomenon associated with the variable structure observer(VSO) is solved using a high-order variable structure observer. Then, the dynamic behavior estimation performance in the high-order variable structure observer is improved by incorporating a neural network algorithm in the FD pipeline. This adaptive technique is also effective in improving the robustness of the fault signal estimation. Moreover, support vector machines (SVMs) that can derive adaptive threshold values are used to categorize faults. To design an effective fault-tolerant controller (FC), an adaptive modern fuzzy backstepping variable structure controller is used in this study. First, a new variable structure controller is designed. Next, to increase robustness and reduce high-frequency oscillations in uncertain conditions, a backstepping algorithm is used in parallel with the variable structure controller to design the backstepping variable structure controller. To design an effective hybrid controller, a fuzzy algorithm is integrated into the backstepping variable structure controller to create a fuzzy backstepping variable structure controller. Then, to improve the robustness and reliability of the FC, a neural adaptive. high-order. variable structure observer is applied to the fuzzy backstepping variable structure controller to design a modern fuzzy backstepping variable structure controller. An adaptive algorithm is used to fine-tune the variable structure coefficients and reduce the effect of faults on the robot manipulator. The effectiveness of the selected algorithm is validated using a PUMA robot manipulator. The neural adaptive. high-order variable structure observer improves the average performance for the identification of various faults by about 27% and 29.2%, compared with the neural high-order variable structure observer and variable structure observer, respectively.

scholarly journals Deep Learning-Based Adaptive Neural-Fuzzy Structure Scheme for Bearing Fault Pattern Recognition and Crack Size Identification

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2102
Author(s):  
Farzin Piltan ◽  
Bach Phi Duong ◽  
Jong-Myon Kim
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Variable Structure ◽  
Crack Size ◽  
High Order ◽  
Support Vector ◽  
Signal Estimation ◽  
Vector Autoregressive ◽  
Order Variable ◽  
Neural Fuzzy

Bearings are complex components with onlinear behavior that are used to mitigate the effects of inertia. These components are used in various systems, including motors. Data analysis and condition monitoring of the systems are important methods for bearing fault diagnosis. Therefore, a deep learning-based adaptive neural-fuzzy structure technique via a support vector autoregressive-Laguerre model is presented in this study. The proposed scheme has three main steps. First, the support vector autoregressive-Laguerre is introduced to approximate the vibration signal under normal conditions and extract the state-space equation. After signal modeling, an adaptive neural-fuzzy structure observer is designed using a combination of high-order variable structure techniques, the support vector autoregressive-Laguerre model, and adaptive neural-fuzzy inference mechanism for normal and abnormal signal estimation. The adaptive neural-fuzzy structure observer is the main part of this work because, based on the difference between signal estimation accuracy, it can be used to identify faults in the bearings. Next, the residual signals are generated, and the signal conditions are detected and identified using a convolution neural network (CNN) algorithm. The effectiveness of the proposed deep learning-based adaptive neural-fuzzy structure technique by support vector autoregressive-Laguerre model was analyzed using the Case Western Reverse University (CWRU) bearing vibration dataset. The proposed scheme is compared to five state-of-the-art techniques. The proposed algorithm improved the average pattern recognition and crack size identification accuracy by 1.99%, 3.84%, 15.75%, 5.87%, 30.14%, and 35.29% compared to the combination of the high-order variable structure technique with the support vector autoregressive-Laguerre model and CNN, the combination of the variable structure technique with the support vector autoregressive-Laguerre model and CNN, the combination of RAW signal and CNN, the combination of the adaptive neural-fuzzy structure technique with the support vector autoregressive-Laguerre model and support vector machine (SVM), the combination of the high-order variable structure technique with the support vector autoregressive-Laguerre model and SVM, and the combination of the variable structure technique with the support vector autoregressive-Laguerre model and SVM, respectively.

scholarly journals An internal teacher for neural computation

1997 ◽  
Vol 20 (4) ◽  
pp. 687-688
Author(s):  
Dario Floreano
Keyword(s):  
Receptive Field ◽  
Long Range ◽  
High Order ◽  
Neural Computation ◽  
The Other ◽  
Computational Experiments ◽  
Multilayer Network ◽  
Order Variable

Contextual signals might supervise the discovery of coherently varying information between cortical modules computing different functions of their receptive field input. This hypothesis is explored in two sets of computational experiments, one studying the effects on learning of long-range unidirectional contextual signals mediated by intervening processors, and the other showing contextually supervised discovery of a high-order variable in a multilayer network.

Tracking Design of an Omni-Direction Autonomous Ground Vehicle by Hierarchical Enhancement Using Fuzzy Second-Order Variable Structure Control

2018 ◽  
Vol 140 (9) ◽  
Author(s):  
Chih-Lyang Hwang ◽  
Yunta Lee
Keyword(s):  
Tracking Error ◽  
Variable Structure Control ◽  
Variable Structure ◽  
Second Order ◽  
Hierarchical Architecture ◽  
Switching Surface ◽  
Ground Vehicle ◽  
Structure Control ◽  
Linear Dynamic ◽  
Order Variable

Owing to the hierarchical architecture of the derived model of the omni-direction autonomous ground vehicle (OD-AGV), the virtual desired trajectory (VDT) is first designed by the first switching surface, which is set as the linear dynamic pose error of the OD-AGV. In sequence, the trajectory tracking control (TTC) is designed by the second switching surface, which is the linear dynamic tracking error of the VDT. To deal with nonlinear time-varying uncertainties including system disturbance and different ground conditions, enhanced fuzzy second-order variable structure control (EF2VSC) is designed into both VDT and TTC. Finally, the experiments for tracking the circular trajectories with different curvatures, traveling velocities, and poses of the OD-AGV are presented to validate the effectiveness and robustness of the proposed hierarchical enhancement using fuzzy second-order variable structure control (HEF2VSC).

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