Fuzzy Neural Network Active Disturbance Rejection Control for Two-Wheeled Self-Balanced Robot


Chao Wang, Xiao Jianliang, and Cheng Zhang, Journal of Information Processing Systems Vol. 18, No. 4, pp. 510-523, Aug. 2022  

10.3745/JIPS.01.0089
Keywords: ADRC, Compensate Dynamically, ESO, FNN, Nonlinear PD Control Rate, TWSBR
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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.


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Cite this article
[APA Style]
Chao Wang, Xiao Jianliang, & and Cheng Zhang (2022). Fuzzy Neural Network Active Disturbance Rejection Control for Two-Wheeled Self-Balanced Robot. Journal of Information Processing Systems, 18(4), 510-523. DOI: 10.3745/JIPS.01.0089.

[IEEE Style]
C. Wang, X. Jianliang and a. C. Zhang, "Fuzzy Neural Network Active Disturbance Rejection Control for Two-Wheeled Self-Balanced Robot," Journal of Information Processing Systems, vol. 18, no. 4, pp. 510-523, 2022. DOI: 10.3745/JIPS.01.0089.

[ACM Style]
Chao Wang, Xiao Jianliang, and and Cheng Zhang. 2022. Fuzzy Neural Network Active Disturbance Rejection Control for Two-Wheeled Self-Balanced Robot. Journal of Information Processing Systems, 18, 4, (2022), 510-523. DOI: 10.3745/JIPS.01.0089.