Dead-Beat Predictive Control of DFIG-DC Based on Data-Driven Neural Network Predictor
- Resource Type
- Conference
- Authors
- Liu, Lu; Wang, Guangqiang; Gu, Nan; Wang, Dan; Peng, Zhouhua
- Source
- 2023 IEEE 14th International Conference on Power Electronics and Drive Systems (PEDS) Power Electronics and Drive Systems (PEDS), 2023 IEEE 14th International Conference on. :1-7 Aug, 2023
- Subject
- Components, Circuits, Devices and Systems
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Voltage measurement
Uncertainty
Induction motors
Simulation
Neural networks
Doubly fed induction generators
Predictive models
Dead-beat predictive control
doubly fed induction generator
fully unknown motor parameters
data-driven neural network predictor
- Language
- ISSN
- 2164-5264
This paper is concerned with the dead-beat predictive control of a doubly fed induction generator in the presence of fully unknown motor parameters. Only the current and voltage values of the rotor are measured in the control system. Firstly, data-driven neural network predictors are proposed to approximate the model uncertainties as well as the unknown control gains. Based on the predictors, a dead-beat predictive controller is designed without any prior knowledge of model parameters. The input-to-state stability of the closed-loop system is proved by using the cascade theory. Simulation results show the effectiveness of the proposed dead-beat predictive control of a doubly fed induction generator based on data-driven neural network predictors.