Freight Train Speed Active Disturbance Rejection Tracking and Wheel Anti-Slip Based on T-S Fuzzy Neural Network
- Resource Type
- Conference
- Authors
- Yi, LingZhi; Yi, Yu; Wang, Yahui
- Source
- 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) Intelligent Transportation Systems (ITSC), 2023 IEEE 26th International Conference on. :203-208 Sep, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Technological innovation
Torque control
Wheels
Observers
Traction motors
Real-time systems
Velocity measurement
- Language
- ISSN
- 2153-0017
A speed tracking control scheme is proposed for freight trains in this paper. This speed tracking scheme approach can prevent the wheel from slipping while controlling the traction motor to drive freight trains at an appropriate speed. direct torque control used by the HXD1 electric traction locomotive is simulated in this strategy to control the asynchronous motor. Velocity tracking control is implemented by a predictive auto disturbance rejection control (PADRC). Through a modified Smith estimator, this PADRC can predict the response of time-delay systems. In addition, an unscented Kalman filter observer is designed. An adaptive parameter adjustment mechanism implemented by affinity propagation T-S fuzzy neural network is also integrated into this observer. It is used to solve the problem that is difficult to measure the radial velocity and creep rate accurately. Using the anti-slip parameters obtained by this observer, the control scheme of anti-skid control is determined. Under wet and dry pavement conditions, an actual speed curve of a freight train is used to simulate and verify the effectiveness of this scheme.