Fuzzy Neural Network Active Disturbance Rejection Control for Two-Wheeled Self-Balanced Robot
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Considering the problems of poor control effect, weak disturbance rejection ability and adaptive ability of twowheeledself-balanced robot (TWSBR) systems on undulating roads, this paper proposes a fuzzy neural networkactive disturbance rejection controller (FNNADRC), that is based on fuzzy neural network (FNN) for onlinecorrection of active disturbance rejection controller (ADRC)’s nonlinear control rate. Firstly, the dynamicmodel of the TWSBR is established and decoupled, the extended state observer (ESO) is used to compensatedynamically and linearize the upright and displacement subsystems. Then, the nonlinear PD control rate andFNN are designed, and the FNN is used to modify the control parameters of the nonlinear PD control rate inreal time. Finally, the proposed control strategy is simulated and compared with the traditional ADRC andfuzzy active disturbance rejection controller (FADRC). The simulation results show that the control effect ofthe proposed control strategy is slightly better than ADRC and FADRC.
Abstract
Considering the problems of poor control effect, weak disturbance rejection ability and adaptive ability of twowheeledself-balanced robot (TWSBR) systems on undulating roads, this paper proposes a fuzzy neural networkactive disturbance rejection controller (FNNADRC), that is based on fuzzy neural network (FNN) for onlinecorrection of active disturbance rejection controller (ADRC)’s nonlinear control rate. Firstly, the dynamicmodel of the TWSBR is established and decoupled, the extended state observer (ESO) is used to compensatedynamically and linearize the upright and displacement subsystems. Then, the nonlinear PD control rate andFNN are designed, and the FNN is used to modify the control parameters of the nonlinear PD control rate inreal time. Finally, the proposed control strategy is simulated and compared with the traditional ADRC andfuzzy active disturbance rejection controller (FADRC). The simulation results show that the control effect ofthe proposed control strategy is slightly better than ADRC and FADRC.
참고문헌 (16)
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