A Task-Space Tracking Control Approach for Duct Cleaning Robot Based on Fuzzy Wavelet Neural Network

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Bu Dexu ◽  
Kong Weiwei ◽  
Qi Yunlong
Keyword(s):  
Neural Network ◽  
Wavelet Neural Network ◽  
Task Space ◽  
External Disturbances ◽  
The Real ◽  
Control Approach ◽  
Cleaning Robot ◽  
Fuzzy Wavelet Neural Network ◽  
Duct Cleaning Robot ◽  
Duct Cleaning

In this study, a task-space adaptive robust control methodology which takes uncertainties and external disturbances into account is proposed for a class of duct cleaning mobile manipulators. First of all, the configuration of the real duct cleaning robot is introduced, and the Jacobian matrix and the dynamic model of the real robotic system are obtained. Then, the structure of adaptive robust controller based on sliding mode control (SMC) approach and the fuzzy wavelet neural network is detailed, the proposed control approach combines the advantages of SMC which can suppress the external disturbances with the fuzzy wavelet neural network which can compensate the uncertainties by its strong ability to approximate a nonlinear function to an arbitrary accuracy, the stability of the whole robotic control system, the uniformly ultimately boundedness of tracking errors, and the boundedness of fuzzy wavelet neural networks weight estimation errors are all guaranteed based on the Lyapunov stability theory. Finally, simulation results are presented to demonstrate the superior performance of the proposed approach, and experiments are given to illustrate that the proposed approach is useful for real duct cleaning robot system with well performance.

Wavelet Neural Network Based Adaptive Robust Control for a Class of MIMO Nonlinear Systems

2011 ◽  
Vol 383-390 ◽  
pp. 290-296
Author(s):  
Yong Hong Zhu ◽  
Wen Zhong Gao
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Wavelet Neural Network ◽  
Adaptive Robust Control ◽  
Loop Control ◽  
Control Approach ◽  
Mimo Nonlinear Systems ◽  
Output Tracking Control ◽  
Dynamic Inverse

Wavelet neural network based adaptive robust output tracking control approach is proposed for a class of MIMO nonlinear systems with unknown nonlinearities in this paper. A wavelet network is constructed as an alternative to a neural network to approximate unknown nonlinearities of the classes of systems. The proposed WNN adaptive law is used to compensate the dynamic inverse errors of the classes of systems. The robust control law is designed to attenuate the effects of approximate errors and external disturbances. It is proved that the controller proposed can guarantee that all the signals in the closed-loop control system are uniformly ultimately bounded (UUB) in the sense of Lyapunov. In the end, a simulation example is presented to illustrate the effectiveness and the applicability of the suggested method.

scholarly journals Control system design of duct cleaning robot capable of overcoming L and T-shaped ducts

2020 ◽  
Vol 9 (2) ◽  
pp. 123
Author(s):  
Myeong In Seo ◽  
Woo Jin Jang ◽  
Junhwan Ha ◽  
Kyongtae Park ◽  
Dong Hwan Kim
Keyword(s):  
Control Method ◽  
Estimation Algorithm ◽  
Location Estimation ◽  
Positional Error ◽  
Cleaning Robot ◽  
Communication Module ◽  
Duct Cleaning Robot ◽  
Robot Position ◽  
Uwb Communication ◽  
Duct Cleaning

This study introduces the control method of duct cleaning robot that enables real-time position tracking and self-driving over L-shaped and T-shaped duct sections. The developed robot has three legs and is designed to flexibly respond to duct sizes. The position of the robot inside the duct is identified using the UWB communication module and the location estimation algorithm. Although UWB communication has relatively large distance error within the metal, the positional error was reduced by introducing appropriate filters to estimate the robot position accurately. TCP/IP communication allows commands to be sent between the PC and the robot and to receive live images of the camera attached to the robot. Using Haar-like and classifiers, the robot can recognize the type of duct that is difficult to overcome, such as L-shaped and T-shaped duct, and it moves successfully inside the duct according to the corresponding moving algorithms.

Development of a Peristaltic-Movement Duct-Cleaning Robot for Application to Actual Environment - Examination of Brush Type and Installation Method to Improve Cleaning Efficiency –

2019 ◽  
Vol 31 (6) ◽  
pp. 781-793
Author(s):  
Fumio Ito ◽  
Takahiko Kawaguchi ◽  
Yasuyuki Yamada ◽  
Taro Nakamura ◽  
◽  
...  
Keyword(s):  
Human Health ◽  
Cross Sectional Area ◽  
Sectional Area ◽  
Cleaning Efficiency ◽  
Cross Sectional ◽  
Cleaning Robot ◽  
Actual Environment ◽  
Peristaltic Movement ◽  
Duct Cleaning Robot ◽  
Duct Cleaning

This paper describes a method to increase both the cleaning performance and speed of a peristaltic duct-cleaning robot, besides the cleaning of a real house duct. Duct piping ventilation is an important component for safeguarding indoor human health. However, dust accumulates inside such ducts during long-term use of ventilation systems. This dust leads to the generation of bacteria, dispersal of which can cause serious human health problems. Therefore, it is necessary to clean such ducts. The ducts used in factories, for example, have a large cross-sectional area and so are easy to clean by conventional duct-cleaning methods. However, as housing ducts have a small cross-sectional area and many curves, they are difficult to clean via the passive method of inserting a cleaning tool through the duct ports. For this reason, the authors attempted to develop a method of duct cleaning using a robot that imitates the peristaltic movement of earthworms. Herein, the authors examine the type and mounting position of the cleaning brush that produces the optimum cleaning efficiency. From this, we confirmed the duct cleanability of the peristaltic robot.

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