An SVM-Based Neural Adaptive Variable Structure Observer for Fault Diagnosis and Fault-Tolerant Control of a Robot Manipulator
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 fa...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/4/1344 |
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| author | Farzin Piltan Alexander E. Prosvirin Muhammad Sohaib Belem Saldivar Jong-Myon Kim |
| author_facet | Farzin Piltan Alexander E. Prosvirin Muhammad Sohaib Belem Saldivar Jong-Myon Kim |
| author_sort | Farzin Piltan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T16:05:33Z |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-13T16:05:33Z |
| publishDate | 2020-02-01 |
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| spelling | doaj.art-5aca6725713d4af3aaa8f34ce041f2c72022-12-22T02:40:24ZengMDPI AGApplied Sciences2076-34172020-02-01104134410.3390/app10041344app10041344An SVM-Based Neural Adaptive Variable Structure Observer for Fault Diagnosis and Fault-Tolerant Control of a Robot ManipulatorFarzin Piltan0Alexander E. Prosvirin1Muhammad Sohaib2Belem Saldivar3Jong-Myon Kim4Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, KoreaDepartment of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, KoreaLahore Garrison University, Lahore 54000, PakistanFaculty of Engineering, Autonomous University of the State of Mexico, Instituto Literario No. 100 Oriente, Toluca 50130, Estado de Mexico, MexicoDepartment of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, KoreaA 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.https://www.mdpi.com/2076-3417/10/4/1344fault diagnosisvariable structure observerbackstepping algorithmneural networkmachine learning techniquesupport vector machineadaptive methodfuzzy logic approach |
| spellingShingle | Farzin Piltan Alexander E. Prosvirin Muhammad Sohaib Belem Saldivar Jong-Myon Kim An SVM-Based Neural Adaptive Variable Structure Observer for Fault Diagnosis and Fault-Tolerant Control of a Robot Manipulator Applied Sciences fault diagnosis variable structure observer backstepping algorithm neural network machine learning technique support vector machine adaptive method fuzzy logic approach |
| title | An SVM-Based Neural Adaptive Variable Structure Observer for Fault Diagnosis and Fault-Tolerant Control of a Robot Manipulator |
| title_full | An SVM-Based Neural Adaptive Variable Structure Observer for Fault Diagnosis and Fault-Tolerant Control of a Robot Manipulator |
| title_fullStr | An SVM-Based Neural Adaptive Variable Structure Observer for Fault Diagnosis and Fault-Tolerant Control of a Robot Manipulator |
| title_full_unstemmed | An SVM-Based Neural Adaptive Variable Structure Observer for Fault Diagnosis and Fault-Tolerant Control of a Robot Manipulator |
| title_short | An SVM-Based Neural Adaptive Variable Structure Observer for Fault Diagnosis and Fault-Tolerant Control of a Robot Manipulator |
| title_sort | svm based neural adaptive variable structure observer for fault diagnosis and fault tolerant control of a robot manipulator |
| topic | fault diagnosis variable structure observer backstepping algorithm neural network machine learning technique support vector machine adaptive method fuzzy logic approach |
| url | https://www.mdpi.com/2076-3417/10/4/1344 |
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