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

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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