Optimization of Urban Rail Automatic Train Operation System Based on RBF Neural Network Adaptive Terminal Sliding Mode Fault Tolerant Control
Aiming at the problem of the large tracking error of the desired curve for the automatic train operation (ATO) control strategy, an ATO control algorithm based on RBF neural network adaptive terminal sliding mode fault-tolerant control (ATSM-FTC-RBFNN) is proposed to realize the accurate tracking co...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Applied System Innovation |
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| Online Access: | https://www.mdpi.com/2571-5577/4/3/51 |
| _version_ | 1829453421957087232 |
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| author | Junxia Yang Youpeng Zhang Yuxiang Jin |
| author_facet | Junxia Yang Youpeng Zhang Yuxiang Jin |
| author_sort | Junxia Yang |
| collection | DOAJ |
| description | Aiming at the problem of the large tracking error of the desired curve for the automatic train operation (ATO) control strategy, an ATO control algorithm based on RBF neural network adaptive terminal sliding mode fault-tolerant control (ATSM-FTC-RBFNN) is proposed to realize the accurate tracking control of train operation curve. On the one hand, considering the state delay of trains in operation, a nonlinear dynamic model is established based on the mechanism of motion mechanics. Then, the terminal sliding mode control principle is used to design the ATO control algorithm, and the adaptive mechanism is introduced to enhance the adaptability of the system. On the other hand, RBFNN is used to adaptively approximate and compensate the additional resistance disturbance to the model so that ATO control with larger disturbance can be realized with smaller switching gain, and the tracking performance and anti-interference ability of the system can be enhanced. Finally, considering the actuator failure and the control input limitation, the fault-tolerant mechanism is introduced to further enhance the fault-tolerant performance of the system. The simulation results show that the control can compensate and process the nonlinear effects of control input saturation, delay, and actuator faults synchronously under the condition of uncertain parameters, external disturbances of the system model and can achieve a small error tracking the desired curve. |
| first_indexed | 2024-03-10T07:54:35Z |
| format | Article |
| id | doaj.art-d32a281f08fc4d9cbe719c4d1744d7e8 |
| institution | Directory Open Access Journal |
| issn | 2571-5577 |
| language | English |
| last_indexed | 2024-03-10T07:54:35Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied System Innovation |
| spelling | doaj.art-d32a281f08fc4d9cbe719c4d1744d7e82023-11-22T11:58:44ZengMDPI AGApplied System Innovation2571-55772021-08-01435110.3390/asi4030051Optimization of Urban Rail Automatic Train Operation System Based on RBF Neural Network Adaptive Terminal Sliding Mode Fault Tolerant ControlJunxia Yang0Youpeng Zhang1Yuxiang Jin2School of Electrical Engineering and Automation, Lanzhou Jiaotong University, Lanzhou 730070, ChinaSchool of Electrical Engineering and Automation, Lanzhou Jiaotong University, Lanzhou 730070, ChinaSchool of Electrical Engineering and Automation, Lanzhou Jiaotong University, Lanzhou 730070, ChinaAiming at the problem of the large tracking error of the desired curve for the automatic train operation (ATO) control strategy, an ATO control algorithm based on RBF neural network adaptive terminal sliding mode fault-tolerant control (ATSM-FTC-RBFNN) is proposed to realize the accurate tracking control of train operation curve. On the one hand, considering the state delay of trains in operation, a nonlinear dynamic model is established based on the mechanism of motion mechanics. Then, the terminal sliding mode control principle is used to design the ATO control algorithm, and the adaptive mechanism is introduced to enhance the adaptability of the system. On the other hand, RBFNN is used to adaptively approximate and compensate the additional resistance disturbance to the model so that ATO control with larger disturbance can be realized with smaller switching gain, and the tracking performance and anti-interference ability of the system can be enhanced. Finally, considering the actuator failure and the control input limitation, the fault-tolerant mechanism is introduced to further enhance the fault-tolerant performance of the system. The simulation results show that the control can compensate and process the nonlinear effects of control input saturation, delay, and actuator faults synchronously under the condition of uncertain parameters, external disturbances of the system model and can achieve a small error tracking the desired curve.https://www.mdpi.com/2571-5577/4/3/51automatic train operation system (ATO)radial basis function neural network (RBFNN)adaptive terminal sliding mode control (ATSMC)fault-tolerant control (FTC)tracking error |
| spellingShingle | Junxia Yang Youpeng Zhang Yuxiang Jin Optimization of Urban Rail Automatic Train Operation System Based on RBF Neural Network Adaptive Terminal Sliding Mode Fault Tolerant Control Applied System Innovation automatic train operation system (ATO) radial basis function neural network (RBFNN) adaptive terminal sliding mode control (ATSMC) fault-tolerant control (FTC) tracking error |
| title | Optimization of Urban Rail Automatic Train Operation System Based on RBF Neural Network Adaptive Terminal Sliding Mode Fault Tolerant Control |
| title_full | Optimization of Urban Rail Automatic Train Operation System Based on RBF Neural Network Adaptive Terminal Sliding Mode Fault Tolerant Control |
| title_fullStr | Optimization of Urban Rail Automatic Train Operation System Based on RBF Neural Network Adaptive Terminal Sliding Mode Fault Tolerant Control |
| title_full_unstemmed | Optimization of Urban Rail Automatic Train Operation System Based on RBF Neural Network Adaptive Terminal Sliding Mode Fault Tolerant Control |
| title_short | Optimization of Urban Rail Automatic Train Operation System Based on RBF Neural Network Adaptive Terminal Sliding Mode Fault Tolerant Control |
| title_sort | optimization of urban rail automatic train operation system based on rbf neural network adaptive terminal sliding mode fault tolerant control |
| topic | automatic train operation system (ATO) radial basis function neural network (RBFNN) adaptive terminal sliding mode control (ATSMC) fault-tolerant control (FTC) tracking error |
| url | https://www.mdpi.com/2571-5577/4/3/51 |
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