基于BP神经网络的三轴增稳云台自抗扰控制

doi:10.16088/j.issn.1001-6600.2020.02.013

Abstract

Abstract: In view of the non-linear characteristics of the three-axis stabilized pan-tilt servo system, the anti-disturbance ability of PD control is poor, and the setting process of the active disturbance rejection control is time-consuming and laborious due to the large number of parameters. By using the global approximation ability and self-learning ability of BP neural network, a composite controller is composed of BP neural network and active disturbance rejection control. All the key parameters of active disturbance rejection control are self-tuned and optimized, which is applied to the three-axis stabilized pan-tilt servo system with Stribeck friction model. The simulation results show that the method is feasible and effective for parameter auto-tuning. Compared with the conventional ADRC with fixed parameters and PD control, it has higher control accuracy and stronger anti-disturbance ability, and has better application value for improving the performance of the stabilized platform.

Key words: stabilized platform, servo system, PD control, active disturbance rejection control, BP neural network

CLC Number:

TP273

LIU Xin, LUO Xiaoshu, ZHAO Shulin. Active Disturbance Rejection Control of Three-AxisStabilized Platform Based on BP Neural Network[J].Journal of Guangxi Normal University(Natural Science Edition), 2020, 38(2): 115-120.

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Active Disturbance Rejection Control of Three-AxisStabilized Platform Based on BP Neural Network

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