JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Fuzzy Neural Network Active Disturbance Rejection Control for Two-Wheeled Self-Balanced Robot |
| 영문제목(English Title) |
Fuzzy Neural Network Active Disturbance Rejection Control for Two-Wheeled Self-Balanced Robot |
| 저자(Author) |
Chao Wang
Xiao Jianliang
Cheng Zhang
|
| 원문수록처(Citation) |
VOL 18 NO. 04 PP. 0510 ~ 0523 (2022. 08) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Considering the problems of poor control effect, weak disturbance rejection ability and adaptive ability of twowheeled self-balanced robot (TWSBR) systems on undulating roads, this paper proposes a fuzzy neural network active disturbance rejection controller (FNNADRC), that is based on fuzzy neural network (FNN) for online correction of active disturbance rejection controller (ADRC)’s nonlinear control rate. Firstly, the dynamic model of the TWSBR is established and decoupled, the extended state observer (ESO) is used to compensate dynamically and linearize the upright and displacement subsystems. Then, the nonlinear PD control rate and FNN are designed, and the FNN is used to modify the control parameters of the nonlinear PD control rate in real time. Finally, the proposed control strategy is simulated and compared with the traditional ADRC and fuzzy active disturbance rejection controller (FADRC). The simulation results show that the control effect of the proposed control strategy is slightly better than ADRC and FADRC. |
| 키워드(Keyword) |
ADRC
Compensate Dynamically
ESO
FNN
Nonlinear PD Control Rate
TWSBR
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| 파일첨부 |
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