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
https://www.mdpi.com/journal/applsci/special_issues/_Robots
Dear Farzin,
Off course your article is good one.
You can see also the brief of content of article given below. Below given article illustrates the methods and algorithms for abstraction, planning, control, and synchronization.
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.
A robot model is a precise representation of a system’s dynamics used to answer questions via analysis and simulation. 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.
In general, Modular Robotic Systems (MRS) span various workspaces, scales, and structures, practical implementations of such systems lag behind their potentials in performing real-world tasks. Thus, MRS solution methods must be able to resolve problems arising from the tightly-coupled kinematics of interconnected modules and their inherent limitations in resources, communication, connection strength, etc. in performing such functions through domain-specific operations including Self-reconfiguration, Flow, Gait, Self-assembly, Self-disassembly, Self-adaptation, Grasping, Collective actuation, and Enveloping.
Several types of control, fault diagnosis, and fault-tolerant control algorithms have been already 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.
Modeling, control, fault diagnosis, and fault-tolerant control of robotic systems are 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 back stepping algorithm), and adaptive techniques are essential elements of fault-tolerant control.
Follow this article. It may be useful for you.
Article Modular Robotic Systems: Methods and algorithms for abstract...
Ashish
- Badges
- Science topic
- Similar topics
- Engineering
- Mechanical Engineering