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

Abstract

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.

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

“…The challenge of vibration signal modeling can be addressed by the mathematical-based system modeling five degrees of freedom vibration bearing modeling. Mathematical-based system modeling (such as five degrees of freedom vibration bearing modeling) is reliable but has some drawbacks, such as the lack of complexity and uncertainty related to modeling [ 13 , 14 , 15 ]. Linear-based system identification techniques (such as the combination of autoregressive with external inputs, and autoregressive with external inputs and Laguerre technique) have been used to address the above challenges [ 15 , 16 , 17 , 18 , 19 ].…”

“…Regarding Figure 1, first, the bearing vibration signal in the normal state was modeled using the SVAL, and the state-space equation of the vibration signal under normal conditions was extracted using Equation (13). In this section, first, an adaptive hybrid observer is recommended for normal and abnormal signals estimation; second, the residual signal, which is the difference between RAW and estimated bearing signals, is generated, and finally, the CNN is represented for fault pattern recognition and crack size identification in the bearing.…”

“…To detect anomalies using the model-based technique, modeling is the critical step. System modeling is categorized into two principal groups: (a) mathematical-based system modeling, such as Newton–Euler, Lagrange, and finite element methods, and (b) system identification approaches, such as autoregressive (AR), autoregressive with external input (ARX), ARX–Laguerre techniques, neural network approach, and fuzzy logic methods [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. To improve the accuracy of signal approximation in nonlinear and nonstationary signals, we propose a combination of autoregressive techniques, namely the Laguerre technique with support vector regression (SVR), which will henceforth be called the support vector autoregressive–Laguerre (SVAL).…”

Fault Diagnosis of Bearings Using an Intelligence-Based Autoregressive Learning Lyapunov Algorithm

“…These techniques have been recommended by several researchers, and include combined signal processing and machine learning approaches, or combined model-based approaches and artificial intelligence methods. In this study, model-based, machine learning, and data-driven schemes are combined for fault detection and identification in bearings [10][11][12][13][14].…”

“…In recent years, researchers have published various articles on the application of model-based techniques for fault diagnosis. These techniques can be divided into two groups: linear maneuverings and nonlinear procedures [13][14][15]. Regardless of whether an algorithm is linear or nonlinear, the first step is to calculate a mathematical model of the system, either directly or indirectly.…”