Moving sliding surfaces for high-order variable structure systems

1999 ◽  
Vol 72 (11) ◽  
pp. 960-970 ◽  
Author(s):  
Dong-Won Park ◽  
Seung-Bok Choi
Keyword(s):  
Variable Structure ◽  
High Order ◽  
Variable Structure Systems ◽  
Sliding Surfaces ◽  
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.

Decentralized Adaptive Control of a Directly Driven Hydraulic Manipulator: Part 1: Theory

1995 ◽  
Vol 209 (3) ◽  
pp. 191-196 ◽  
Author(s):  
K A Edge ◽  
F Gomes de Almeida
Keyword(s):  
Adaptive Control ◽  
Variable Structure ◽  
System Stability ◽  
Design Parameters ◽  
Variable Structure Systems ◽  
Model Following ◽  
Reference Type ◽  
Control Objective ◽  
Manipulator Design ◽  
Following Error

A new approach to adaptive control of manipulators is presented in this paper. The proposed controller for each individual axis is of the model reference type, designed through the use of variable structure systems theory. A novel feature of the controller is the introduction of a series-parallel model of the model-following error. The use of this model ensures system stability even if the manipulator design parameters or payload bounds are exceeded. Chattering of the system, associated with variable structure systems, is eliminated by arranging for the control objective to be physically achievable.

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