scholarly journals Real-Time Stripe Width Computation Using Back Propagation Neural Network for Adaptive Control of Line Structured Light Sensors

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2618 ◽  
Author(s):  
Jingbo Zhou ◽  
Laisheng Pan ◽  
Yuehua Li ◽  
Peng Liu ◽  
Lijian Liu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Real Time ◽  
Exposure Time ◽  
Control Method ◽  
Structured Light ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Stripe Width ◽  
Accuracy Result

A line structured light sensor (LSLS) is generally constituted of a laser line projector and a camera. With the advantages of simple construction, non-contact, and high measuring speed, it is of great perspective in 3D measurement. For traditional LSLSs, the camera exposure time is usually fixed while the surface properties can be varied for different measurement tasks. This would lead to under/over exposure of the stripe images or even failure of the measurement. To avoid these undesired situations, an adaptive control method was proposed to modulate the average stripe width (ASW) within a favorite range. The ASW is first computed based on the back propagation neural network (BPNN), which can reach a high accuracy result and reduce the runtime dramatically. Then, the approximate linear relationship between the ASW and the exposure time was demonstrated via a series of experiments. Thus, a linear iteration procedure was proposed to compute the optimal camera exposure time. When the optimized exposure time is real-time adjusted, stripe images with the favorite ASW can be obtained during the whole scanning process. The smoothness of the stripe center lines and the surface integrity can be improved. A small proportion of the invalid stripe images further proves the effectiveness of the control method.

Multisensor information-based adaptive control method for cutting head speed of roadheader

2020 ◽  
pp. 095440622095249
Author(s):  
Pengjiang Wang ◽  
Yang Shen ◽  
Rui Li ◽  
Kai Zong ◽  
Shichen Fu ◽  
...  
Keyword(s):  
Adaptive Control ◽  
Control Method ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Fuzzy Logic Controllers ◽  
Control Effect ◽  
Cutting Efficiency ◽  
Cutting Head ◽  
Pi Controllers ◽  
Particle Swarm Optimisation Algorithm

An adaptive control method to improve the cutting head speed of roadheaders using multisensor information is proposed, so as to solve the problems of low cutting efficiency and low intelligence of roadheaders during underground tunnelling. The operation of a roadheader is analysed, and a control strategy for its cutting head speed is proposed. In addition, the cutting head speed is categorised into five gears according to the multisensor information of different cutting states. The controller for speed estimation is designed using a back propagation neural network optimised using an improved particle swarm optimisation algorithm. A control system is established in MATLAB to analyse the effectiveness of the method. The simulation results show that an IPSO-BP controller has the best control effect and can attain the target speed. The response time was lower than those of fuzzy logic controllers and traditional PI controllers by 46% and 68%, respectively, and the overshoot decreased by 4.69% and 12.19%, respectively. Furthermore, experimental research verified the effectiveness of this method. This method can adaptively adjust the cutting head speed of a roadheader using multisensor information and is important (both theoretical and practically) for extending the service life of roadheaders and improving tunnelling efficiency.

Position and pose measurement of 3-PRS ankle rehabilitation robot based on deep learning

2020 ◽  
Vol 13 ◽  
Author(s):  
Guoqiang Chen ◽  
Hongpeng Zhou ◽  
Junjie Huang ◽  
Mengchao Liu ◽  
Bingxin Bai
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Medical Staff ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Good Effect ◽  
Rehabilitation Robot ◽  
Ankle Rehabilitation ◽  
Robot Position

Introduction: The position and pose measurement of the rehabilitation robot plays a very important role in patient rehabilitation movement, and the non-contact real-time robot position and pose measurement is of great significance. Rehabilitation training is a relatively complicated process, so it is very important to detect the training process of the rehabilitation robot in real time and accuracy. The method of the deep learning has a very good effect on monitoring the rehabilitation robot state. Methods: The structure sketch and the 3D model of the 3-PRS ankle rehabilitation robot are established, and the mechanism kinematics is analyzed to obtain the relationship between the driving input - the three slider heights - and the position and pose parameters. The whole network of the position and pose measurement is composed of two stages: (1) measuring the slider heights using the CNN based on the robot image and (2) calculating the position and pose parameter using the BPNN based on the measured slider heights from the CNN. According to the characteristics of continuous variation of the slider heights, a regression CNN is proposed and established to measure the robot slider height. Based on the data calculated by using the inverse kinematics of the 3-PRS ankle rehabilitation robot, a BPNN is established to solve the forward kinematics for the position and pose. Results: The experimental results show that the regression CNN outputs the slider height and then the BPNN accurately outputs the corresponding position and pose. Eventually, the position and pose parameters are obtained from the robot image. Compared with the traditional robot position and pose measurement method, the proposed method has significant advantages. Conclusion: The proposed 3-PRS ankle rehabilitation position and pose method can not only shorten the experiment period and cost, but also get excellent timeliness and precision. The proposed approach can help the medical staff to monitor the status of the rehabilitation robot and help the patient rehabilitation in training. Discussion: The goal of the work is to construct a new position and pose detection network based on the combination of the regression convolutional neural network (CNN) and the back propagation neural network (BPNN). The main contribution is to measure the position and pose of the 3-PRS ankle rehabilitation robot in real time, which improves the measurement accuracy and the efficiency of the medical staff work.

Real-Time Reliability Assessment of Wind Turbine Components Using a Back-Propagation Neural Network and SCADA Data

2021 ◽  
Author(s):  
Shenglei Du ◽  
Jingmei Guo ◽  
Lin Yi ◽  
Chen Zhang ◽  
Shi Liu
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Real Time ◽  
Back Propagation ◽  
Reliability Assessment ◽  
Back Propagation Neural Network ◽  
Condition Assessment ◽  
Assessment Method ◽  
Operating Conditions ◽  
Research Trend

Abstract The high cost of operation and maintenance (O&M) management has become an important factor hindering the sustainable development of the wind power industry. Performing accurate condition assessment of wind turbine components to optimize the structural design and O&M strategy has become a research trend. However, the random and varying operating conditions of wind turbines make this problem difficult and challenging. A Supervisory Control and Data Acquisition (SCADA) system collects signals that contain a large amount of raw and useful information from critical wind turbine sub-assemblies. Extracting key information from the SCADA data is an economical and effective way for condition assessment. A real-time reliability assessment method of wind turbine components using a Back-Propagation Neural Network (BPNN) and SCADA data is presented in this paper. The normal behavior models are established with the processed SCADA data, and the real-time reliability of wind turbine components are assessed based on the prediction result. For verification, the BPNN-based reliability assessment method is applied to a gearbox with real SCADA data of a 1.5MW onshore wind turbine located along the southeast coast of China. The results show the capability of the proposed model in assessing the reliability of wind turbine components continuously and in real time.

Center Extraction of Structured Light Stripe Based on Back Propagation Neural Network

2019 ◽  
Vol 39 (12) ◽  
pp. 1212005
Author(s):  
李玥华 Li Yuehua ◽  
刘朋 Liu Peng ◽  
周京博 Zhou Jingbo ◽  
任有志 Ren Youzhi ◽  
靳江艳 Jin Jiangyan
Keyword(s):  
Neural Network ◽  
Structured Light ◽  
Back Propagation ◽  
Back Propagation Neural Network

Active Vibration Control of a Modular Robot Combining a Back-Propagation Neural Network with a Genetic Algorithm

2005 ◽  
Vol 11 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Yangmin Li ◽  
Yugang Liu ◽  
Xiaoping Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Vibration Control ◽  
Control Method ◽  
Active Vibration Control ◽  
Synthesis Method ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Modular Robot ◽  
Analysis Experiment

In this paper, a genetic algorithm based back-propagation neural network suboptimal controller is developed to control the vibration of a nine-degrees-of-freedom modular robot. A finite-element method is used to model the modules of the robot, and the entire system dynamic equation is established using the substructure synthesis method. Then the joint stiffness parameters are identified based on the experimental modal analysis experiment. After modeling the whole structure with the models of the robotic modules and the joint parameters, simulations of the vibration control for the modular robot in several configurations are carried out. It is shown that the control method presented in this paper is effective at suppressing the residual vibrations of the modular robot.

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