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Robots: Modeling, Control, Fault Diagnosis, and Fault-Tolerant Control

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 4253

Special Issue Editors

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Guest Editor
Department of Electrical, Electronics, and Computer Engineering; University of Ulsan, Ulsan, Korea
Interests: nonlinear and advance control; robotics and control; artificial intelligence; machine fault diagnosis; fault-tolerant control; condition monitoring; system modeling
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Special Issue Information

Dear Colleagues,

Robotics technology influences every aspect of work and home. Robotics has the potential to positively transform lives and work practices, raise efficiency and safety levels, and provide enhanced levels of service. Further, robotics is set to become the driving technology underpinning a whole new generation of autonomous devices and cognitive artifacts that, through their learning capabilities, interact seamlessly with the world around them, and, hence, provide the missing link between the digital and physical world. Robots are often used in various industries, such as packaging and automotive manufacturing. The dynamic behavior of robots is entirely nonlinear, coupled, and time-variant, which causes several challenges in modeling, control, fault detection, estimation, identification, and tolerant control. Heavy-duty cycles, overloading, poor installation, and operator errors can be caused by various defects: sensor faults, actuator failures, and plant faults.

A model is a precise representation of a system’s dynamics used to answer questions via analysis and simulation. The model we choose depends on the questions that we wish to answer, and so there may be multiple models for a single physical system, with different levels of fidelity depending on the phenomena of interest. A model is a mathematical representation of a physical, biological, or information system. Models allow us to reason about a system and make predictions about how a system will behave. System modeling may be used in control, fault diagnosis, and fault-tolerant control. System modeling has been divided into two principal techniques: (a) Physical-based system modeling, which uses a disassembled robot to extract the mathematical formulation, and (b) signal-based system identification, which uses various identification techniques.

Several types of control, fault diagnosis, and fault-tolerant control algorithms have been developed for robots. These methods are divided into four main classes: (a) signal-based, (b) model-reference, (c) knowledge-based, and (d) hybrid techniques. All methods for fault diagnosis have specific advantages and challenges. Signal-based fault diagnosis extracts the main features from output signals. Because of the presence of disturbances, the performance of this method is degraded. Knowledge-based fault diagnosis is highly dependent on the historical data used for training, which incur high computational costs for real-time data. The model-reference method identifies faults using a small dataset, but it requires an accurate system model. Hybrid control, fault detection, estimation, and identification techniques use a combination of high-performance methods to design a stable and reliable technology.

This Special Issue focuses on mechanics, control, modeling, fault diagnosis, and fault-tolerant control for robotic systems. Papers specifically addressing the theoretical, experimental, practical, and technological aspects of modeling, control, fault diagnosis, and fault-tolerant control of robotic systems and extending concepts and methodologies from classical techniques to hybrid methods will be highly suitable for this Special Issue. Potential themes include, but are not limited to, the following: modeling, control, fault diagnosis, and fault-tolerant control of robotic systems based on various techniques such as model-based techniques (e.g., sliding mode technique, feedback linearization algorithm, backstepping technique, Lyapunov-based method, etc.), knowledge-based algorithm (e.g., deep learning, transfer learning, fuzzy algorithm, neural network methods, and neuro-fuzzy inference techniques), hybrid methods (e.g., intelligent sliding mode technique, intelligent feedback linearization method, and intelligent backstepping algorithm), and adaptive techniques.

Prof. Dr. Jong-Myon Kim
Dr. Farzin Piltan
Guest Editors

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  • Robust/nonlinear control algorithms 
  • Condition monitoring
  • Fault diagnosis 
  • Fault-tolerant control 
  • Model-based Techniques 
  • Artificial intelligence methods 
  • Hybrid methods 
  • Adaptive techniques

Published Papers (1 paper)

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26 pages, 4843 KiB  
An SVM-Based Neural Adaptive Variable Structure Observer for Fault Diagnosis and Fault-Tolerant Control of a Robot Manipulator
by Farzin Piltan, Alexander E. Prosvirin, Muhammad Sohaib, Belem Saldivar and Jong-Myon Kim
Appl. Sci. 2020, 10(4), 1344; - 16 Feb 2020
Cited by 20 | Viewed by 3561
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 [...] Read more.
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. Full article
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