Open-Closed-Loop Iterative Learning Control with the System Correction Term for the Human Soft Tissue Welding Robot in Medicine

Wu, Yang; Yang, Min; Zhang, Jiancheng

doi:10.1155/2020/2458318

Cited by 3 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other fields, robotic welding vision sensors can easily address complex, difficult-to-reach, and hazardous welding scenarios through visual perception [ 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 ]. By accurately detecting, recognizing, and modeling the object to be welded, the sensors can comprehensively grasp the structure, spatial relationships, and positioning of the object, facilitating the precise control of the welding torch and ensuring optimal welding results.…”

Section: Robotic Welding Sensors In Industrial Applicationsmentioning

confidence: 99%

Visual Sensing and Depth Perception for Welding Robots and Their Industrial Applications

Wang,

Li,

2023

Sensors

View full text Add to dashboard Cite

With the rapid development of vision sensing, artificial intelligence, and robotics technology, one of the challenges we face is installing more advanced vision sensors on welding robots to achieve intelligent welding manufacturing and obtain high-quality welding components. Depth perception is one of the bottlenecks in the development of welding sensors. This review provides an assessment of active and passive sensing methods for depth perception and classifies and elaborates on the depth perception mechanisms based on monocular vision, binocular vision, and multi-view vision. It explores the principles and means of using deep learning for depth perception in robotic welding processes. Further, the application of welding robot visual perception in different industrial scenarios is summarized. Finally, the problems and countermeasures of welding robot visual perception technology are analyzed, and developments for the future are proposed. This review has analyzed a total of 2662 articles and cited 152 as references. The potential future research topics are suggested to include deep learning for object detection and recognition, transfer deep learning for welding robot adaptation, developing multi-modal sensor fusion, integrating models and hardware, and performing a comprehensive requirement analysis and system evaluation in collaboration with welding experts to design a multi-modal sensor fusion architecture.

show abstract

Section: Robotic Welding Sensors In Industrial Applicationsmentioning

confidence: 99%

Visual Sensing and Depth Perception for Welding Robots and Their Industrial Applications

Wang,

Li,

2023

Sensors

View full text Add to dashboard Cite

show abstract

Friction-free approach for a servo system: Design of a robust sliding-mode control based on a recurrent fuzzy neural network

Ngo

Nguyen²,

Nguyễn³

2022

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

Uncertainties such as disturbance and nonlinear friction always exist in physical motion control systems and degrade the precision of their tracking performance. In this research, a practical control strategy is integrated with a continuously differentiable friction model for a high-performance AC servo motor that performs with a continuous control input and is, hence, more properly suited for industrial applications. To further lessen the external disturbance and improve the tracking accuracy, a recurrent fuzzy neural network scheme was developed to approximate the system uncertainties, and its stability was guaranteed by the design of robust laws. The proposed controller was implemented on a high-performance digital signal processor (TMS320C6727). The results of the tracking simulations and experiments show that the proposed control scheme has a feasible and effective design for engineering servo system. In comparison with other algorithms, our approach has smaller values for both the peak and the root mean square of the tracking error while maintaining friction compensation, and it can potentially have a software implementation.

show abstract

PID-Type Iterative Learning Control for Output Tracking Gearing Transmission Systems

Anh¹,

Nga

Hoc

2021

IJRCS

View full text Add to dashboard Cite

In this paper, we propose a modified version of the Proportional Integral Derivative (PID)-type iterative learning algorithm. It is very simple to implement on a digital control device for tracking control a continuous-time system. Matlab software is used to model and simulate control algorithms. The simulative application of it to control a gearing transmission system, such that its output response follows the desired trajectory, is then carried out computationally. Obtained studying results proves that this proposed iterative learning algorithm has provided a good output tracking behavior as expected and which is robust in the sense of reducing external disturbance effects.

show abstract

Open-Closed-Loop Iterative Learning Control with the System Correction Term for the Human Soft Tissue Welding Robot in Medicine

Cited by 3 publications

References 36 publications

Visual Sensing and Depth Perception for Welding Robots and Their Industrial Applications

Visual Sensing and Depth Perception for Welding Robots and Their Industrial Applications

Friction-free approach for a servo system: Design of a robust sliding-mode control based on a recurrent fuzzy neural network

PID-Type Iterative Learning Control for Output Tracking Gearing Transmission Systems

Contact Info

Product

Resources

About