Data-Driven Model-Free Adaptive Sliding Mode Control Based on FFDL for Electric Multiple Units
Source abstract
Abstract
The electric multiple units (EMUs) have become a very convenient and powerful means of transportation in our daily life. Safe and punctual trajectory tracking control is the key to improve the performance of the EMUs system, but it is difficult to realize due to the influence of environmental uncertainty, coupling and nonlinearity. In this paper, a model-free adaptive sliding mode control (MFASMC) method is proposed for the EMUs. This method can solve the dependence of the model-based control method on the train model and eliminate the influence of external disturbances on the robust performance of the system. In this method, the running process of the EMUs is equivalent to a full format dynamic linearization (FFDL) data model, and a model-free adaptive controller (MFAC) is designed based on the data model. Then, to reduce the influence of measurement disturbance and improve the robustness of the system, a discrete sliding mode control (SMC) algorithm is introduced. Furthermore, to prevent the control input from being too large, the parameter estimation error is introduced as an additional correction term of the algorithm. In the end, the simulation experiment is carried out with CRH380A EMUs as the object. Compared with the traditional MFAC and the traditional SMC, the speed tracking effect of each power unit of the MFASMC algorithm is more effective, the change of control force is stable, the acceleration meets the requirements of driving, and has a strong inhibitory effect on external disturbances.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Railway Systems and Energy Efficiency · Electric and Hybrid Vehicle Technologies · Vehicle Dynamics and Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China
State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure, East China Jiaotong University, Nanchang 330013, China
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
Funding
National Natural Science Foundation of China · Training Program for Academic and Technical Leaders of Major Disciplines of Jiangxi Province · Natural Science Foundation Project of Liaoning Province
References (35)
Show source-provided references
1. Multiple-model based diagnosis of multiple faults with high speed train applications using second level adaptation10.1109/TIE.2020.2994867 ↗
2. Generalized predictive control with dual adaptation method of high speed train
3. Generalized predictive control tuning for high-speed train based on controller matching method
4. Jia, C. (2020). Nonlinear Predictive Control for Automatic Train Operation with Consideration of Safety Constraints and Multi-Point Model. [Ph.D. Dissertation, Beijing Jiaotong University].
5. Slip velocity tracking control of high-speed train using dynamic surface method
6. Distributed sliding mode control strategy for high-speed EMU strong coupling model
7. Multi-ANFIS model based synchronous tracking control of high-speed electric multiple unit10.1109/TFUZZ.2017.2725819 ↗
8. Predictive control using a distributed model for electric multiple unit
9. Hou, Z.S. (1994). Nonlinear System Parameter Identification, Adaptive Control and Model Free Adaptive Learning Control. [Ph.D. Thesis, Northeastern University].
10. Ma, Y.S., Che, W.W., and Deng, C. (2021). Distributed model-free adaptive control for learning nonlinear MASs under DoS attacks. IEEE Trans. Neural Netw. Learn. Syst., early access.
11. Dynamic event-triggered model-free adaptive control for nonlinear CPSs under aperiodic DoS attacks10.1016/j.ins.2022.01.009 ↗
12. Apply model-free adaptive control approach for mobile robot path following10.2991/jrnal.k.200909.010 ↗
13. Model-free adaptive sliding mode control with adjustable funnel boundary for robot manipulators with uncertainties10.1063/5.0037054 ↗
14. Model-Free adaptive control method for a class of unknown MIMO systems with measurement noise and application to quadrotor aircraft10.1049/iet-cta.2020.0073 ↗
15. Model-free adaptive robust control design for a small unmanned helicopter
16. Research on automatic train operation based on model-free adaptive control
17. Yang, W., Yin, C.K., and Hou, Z.S. (2012, January 25–27). A novel energy efficient operation strategy for a train based on model-free adaptive predictive control. Proceedings of the 31st Chinese Control Conference, Hefei, China.
18. Model-Free adaptive fault-tolerant control for multiple point-mass subway trains with speed and traction/braking force constraints10.1016/j.ifacol.2020.12.2239 ↗
19. Model-free adaptive fault-tolerant control for subway trains with speed and traction/braking force constraints10.1049/iet-cta.2019.1161 ↗
20. Data-driven model-dree adaptive fault tolerant control for high-speed train
21. Maximum likelihood identification of nonlinear model for high-speed train
22. Modeling and tracking control for braking process of high-speed electric multiple unit
23. Research on train dynamic coupling strategy based on distributed model predictive control10.1088/1742-6596/2183/1/012029 ↗
24. Wu, Q., Cole, C., Spiryagin, M., Chang, C., Wei, W., Ursulyak, L., Shvets, A., Murtaza, M.A., Mirza, I.M., and Zhelieznov, K. (2021). Freight train air brake models. Int. J. Rail Transp., 1–49.10.1080/23248378.2021.2006808 ↗
25. Adaptive Control Design and Evaluation for Multibody High-Speed Train Dynamic Models10.1109/TCST.2020.2991119 ↗
26. Model-free adaptive control for unknown MIMO nonaffine nonlinear discrete-time systems with experimental validation10.1109/TNNLS.2020.3043711 ↗
27. On model-free adaptive control and its stability analysis10.1109/TAC.2019.2894586 ↗
28. Model-free adaptive integral terminal sliding mode predictive control for a class of discrete-time nonlinear systems10.1016/j.isatra.2019.02.033 ↗
29. Observer-based sampled-data model-free adaptive control for continuous-time nonlinear nonaffine systems with input rate constraints10.1109/TSMC.2020.2982491 ↗
30. Speed tracking control using an ANFIS model for high-speed electric multiple unit10.1016/j.conengprac.2013.10.006 ↗
31. MIMO evolution model-based coupled fault estimation and adaptive control with high-speed train applications10.1109/TCST.2017.2735360 ↗
32. Optimization of model-free adaptive controller using differential evolution method
33. Positive consensus of multi-agent systems with hierarchical control protocol10.1016/j.automatica.2022.110191 ↗
34. Cross-dimensional formation control of second-order heterogeneous multi-agent systems10.1016/j.isatra.2022.02.036 ↗
35. A simplified Markov-based approach for safety integrity level verification10.1016/j.jlp.2014.03.013 ↗