scholarly journals Adaptive Output Feedback Control for the Trajectory Tracking of High-Speed Trains with Disturbance Uncertainties on the Basis of Neural Network Observers

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yang Liu ◽  
Weidong Li
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Output Feedback ◽  
High Speed ◽  
Tracking System ◽  
External Disturbance ◽  
Adaptive Observer ◽  
Lyapunov Methods ◽  
Model Parameter ◽  
High Speed Trains

The dynamic model of high-speed trains (HSTs) is nonlinear and uncertain; hence, with the decrease in the running interval of HSTs, an accurate and safe train operation control algorithm is required. In this study, an adaptive output feedback trajectory tracking control method for HSTs is proposed on the basis of neural network observers. The proposed method aims to solve problems, such as the immeasurable speed, model parameter disturbance, and unknown external disturbance of HSTs. In this method, a neural network adaptive observer is designed to estimate the velocity of an HST. Another neural network model is used to approximate the model uncertainties. Moreover, a robust controller is constructed by considering the train position and velocity tracking errors. In the proposed observer/controller, the bound function of estimator errors is introduced to increase the accuracy and safety of the tracking system. Furthermore, the adaptive update value of the neural networks, output weights, and bound function are performed online. All adaptive algorithms and the observer/controller are synthesized in nonlinear control systems. The error signals of the closed-loop trajectory tracking system are uniformly and eventually bounded through a formal proof on the basis of the Lyapunov methods. Simulation examples illustrate that the proposed controller is robust and has excellent tracking accuracy for system model parameter and external disturbance.

scholarly journals Weighted Multiple-Model Neural Network Adaptive Control for Robotic Manipulators with Jumping Parameters

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Jiazhi Li ◽  
Weicun Zhang ◽  
Quanmin Zhu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
External Disturbance ◽  
Lyapunov Stability Theory ◽  
Robotic Manipulators ◽  
Adaptive Observer ◽  
Multiple Model ◽  
Neural Network Controller ◽  
The Neural Network ◽  
The Stability

This study addresses the tracking control issue for n-link robotic manipulators with largely jumping parameters. Based on radial basis function neural networks (RBFNNs), we propose weighted multiple-model neural network adaptive control (WMNNAC) approach. To cover the variation ranges of the parameters, different models of robotic are constructed. Then, the corresponding local neural network controller is constructed, in which the neural network has been used to approximate the uncertainty part of the control law, and an adaptive observer is implemented to estimate the true external disturbance. The WMNNAC strategy with improved weighting algorithm is adopted to ensure the tracking performance of the robotic manipulator system when parameters jump largely. Through the Lyapunov stability theory and the method of virtual equivalent system (VES), the stability of the closed-loop system is proved. Finally, the simulation results of a two-link manipulator verify the feasibility and efficiency of the proposed WMNNAC strategy.

scholarly journals Three-dimensional mandibular motion trajectory-tracking system based on BP neural network

2020 ◽  
Vol 17 (5) ◽  
pp. 5709-5726
Author(s):  
Sukun Tian ◽  
◽  
Ning Dai ◽  
Linlin Li ◽  
Weiwei Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Bp Neural Network ◽  
Tracking System ◽  
Three Dimensional ◽  
Motion Trajectory

Neuroadaptive Control for Trajectory Tracking of Indirect Drive Robots

2017 ◽  
Author(s):  
Yu Zhao ◽  
Xiaowen Yu ◽  
Masayoshi Tomizuka
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
High Speed ◽  
Lyapunov Stability Theory ◽  
Industrial Robots ◽  
Control Approach ◽  
Data Driven Approach ◽  
Indirect Drive ◽  
Low Torque ◽  
Neuroadaptive Control

Most industrial robots are indirect drive robots, which utilize low torque and high speed motors. Indirect drive robots have gear reducers between the motors and links. Due to the flexibility of transmission units, it is difficult to achieve high accuracy for trajectory tracking. In this paper, a neuroadaptive control, which is essentially a neural network (NN) based adaptive back-stepping control approach, is proposed to deal with this problem. The stability of the proposed approach is analysed using Lyapunov stability theory. A data-driven approach is also proposed for the training of the neural network. The effectiveness of the proposed controller is verified by simulation of both single joint and 6-axis industrial robots.

scholarly journals Enhancing Railway Pantograph Carbon Strip Prognostics with Data Blending through a Time-Delay Neural Network Ensemble

2020 ◽  
Vol 12 (1) ◽  
pp. 9
Author(s):  
Alexandre Trilla ◽  
Ver´onica Fern´andez ◽  
Xavier Cabr´e
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Energy Supply ◽  
High Speed ◽  
Potential Utility ◽  
High Current ◽  
Neural Network Ensemble ◽  
Neural Ensemble ◽  
High Speed Trains ◽  
Thickness Measurements

Energy supply for high-speed trains is mainly attained witha high-voltage catenary (i.e., the source on the infrastructure)in contact with a sliding pantograph (i.e., the drain on therolling-stock vehicle). The friction between these two elementsis minimised with a carbon strip that the pantographequips. In addition to erosion, this carbon strip is also subjectto abrasion due to the high current that flows from the catenaryto the train. Therefore, it is of utmost importance to keepthe degradation of the carbon material under control to guaranteethe reliability of the railway service. To attain this goal,this article explores an accurate (i.e., uncertainty bounded)predictive method based on a robust online non-linear multivariateregression technique, considering some factors thatmay have an impact on the degradation on the carbon strip,such as the seasonal condition of the contact wire, which maydevelop an especially critical ice build-up in the winter. Theproposed approach uses a neural ensemble to integrate allthese sources of potential utility with the carbon strip data,which is convoluted in time with a set of spreading filters toincrease the overall robustness. Finally, the article evaluatesthe effectiveness of this prognosis approach with a dataset ofpantograph carbon thickness measurements over a year at thefleet level. The results of the analysis prove that it is definitelypossible to deploy a fine prediction, and thus yield a new avenuefor business improvement through the application of thepredictive maintenance approach to pantograph carbon strips.

scholarly journals Distributed Cooperative Sliding Mode Fault-Tolerant Control for Multiple High-Speed Trains Based on Actor-Critic Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiangyu Kong ◽  
Tong Zhang
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
High Speed ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Tracking Error ◽  
Fault Tolerant Control ◽  
Actuator Faults ◽  
High Speed Trains ◽  
Critic Neural Network

This article investigates the cooperative fault-tolerant control problem for multiple high-speed trains (MHSTs) with actuator faults and communication delays. Based on the actor-critic neural network, a distributed sliding mode fault-tolerant controller is designed for MHSTs to solve the problem of actuator faults. To eliminate the negative effects of unknown disturbances and time delay on train control system, a distributed radial basis function neural network (RBFNN) with adaptive compensation term of the error is designed to approximate the nonlinear disturbances and predict the time delay, respectively. By calculating the tracking error online, an actor-critic structure with RBFNN is used to estimate the switching gain of the distributed controller, which reduces the chattering phenomenon caused by sliding mode control. The global stability and ultimate bounded of all signals of the closed-loop system are proposed with strict mathematic proof. Simulations show that the proposed method has superior effectiveness and robustness compared with other fault-tolerant control methods, which ensures the safe operation of MHSTs under moving block conditions.

Passenger comfort evaluation of the stowing and unloading luggage task in high-speed trains: Fusion of R-DEMATEL and FNN

2021 ◽  
pp. 1-13
Author(s):  
Hang Zhao ◽  
Jianjie Chu ◽  
Rong Mo ◽  
Chen Chen ◽  
Ning Ding
Keyword(s):  
Neural Network ◽  
High Speed ◽  
Evaluation Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Passenger Comfort ◽  
Evaluation Indexes ◽  
Comfort Evaluation ◽  
Fuzzy Neural ◽  
High Speed Trains

At present, high-speed trains have become popular modern transportation. As a significant part of the high-speed train riding activity, the stowing and unloading luggage task has its characteristics. To comprehensively and reasonably evaluate passenger comfort of the stowing and unloading luggage task in high-speed trains. In this paper, passenger behavior characteristics are firstly analyzed by the author, the theoretical architecture of passenger comfort evaluation is constructed with the perspective of product aesthetics and ergonomics, and then the process of the passenger comfort evaluation is put forward. Secondly, a combination of Rough Number (RN) and Decision Making Trial and Evaluation Laboratory (DEMATEL) (i.e. R-DEMATEL) is utilized to solve the centrality degree of comfort influencing factors and determine comfort evaluation indexes. Furthermore, the passenger comfort evaluation model with Fuzzy Neural Network (FNN) is constructed and trained. After that, the sample data of the evaluation are collected through the simulated experiment of the stowing and unloading luggage task, and they are trained with FNN comparing to Back Propagation Neural Network (BPNN). Eventually, the result of examples testing is verified that the effectiveness of the proposed method.

scholarly journals A Novel Deep-Learning-Based Bug Severity Classification Technique Using Convolutional Neural Networks and Random Forest with Boosting

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2964 ◽  
Author(s):  
Ashima Kukkar ◽  
Rajni Mohana ◽  
Anand Nayyar ◽  
Jeamin Kim ◽  
Byeong-Gwon Kang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
High Speed ◽  
Tracking System ◽  
Bug Reports ◽  
Severity Classification ◽  
Bug Report ◽  
N Gram

The accurate severity classification of a bug report is an important aspect of bug fixing. The bug reports are submitted into the bug tracking system with high speed, and owing to this, bug repository size has been increasing at an enormous rate. This increased bug repository size introduces biases in the bug triage process. Therefore, it is necessary to classify the severity of a bug report to balance the bug triaging process. Previously, many machine learning models were proposed for automation of bug severity classification. The accuracy of these models is not up to the mark because they do not extract the important feature patterns for learning the classifier. This paper proposes a novel deep learning model for multiclass severity classification called Bug Severity classification to address these challenges by using a Convolutional Neural Network and Random forest with Boosting (BCR). This model directly learns the latent and highly representative features. Initially, the natural language techniques preprocess the bug report text, and then n-gram is used to extract the features. Further, the Convolutional Neural Network extracts the important feature patterns of respective severity classes. Lastly, the random forest with boosting classifies the multiple bug severity classes. The average accuracy of the proposed model is 96.34% on multiclass severity of five open source projects. The average F-measures of the proposed BCR and the existing approach were 96.43% and 84.24%, respectively, on binary class severity classification. The results prove that the proposed BCR approach enhances the performance of bug severity classification over the state-of-the-art techniques.

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