JACIII Vol.26 No.6 pp. 944-951
doi: 10.20965/jaciii.2022.p0944


2-DOF Fractional Order PID Control Based on BP Neural Network for Atomic Force Microscope

Shujun Chang*,†, Chao Peng**, Shiqiang Dai**, and Jianyu Wang**

*Zhongshan Institute, University of Electronic Science and Technology of China
1 Xueyuan Road, Shiqi District, Zhongshan City, Guangdong 528400, China

**School of Automation Engineering, University of Electronic Science and Technology of China
2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan 611731, China

Corresponding author

July 12, 2021
June 20, 2022
November 20, 2022
two-degree of freedom fractional PID control, back propagation neural network, atomic force microscope system, trajectory tracking

To enhance trajectory tracking performance of atomic force microscope system, a two-degree of freedom fractional order PID (2-DOF FOPID) control approach based on back propagation (BP) neural network is proposed in this paper. At first, principle and structure of the proposed control approach is presented. Then, 2-DOF FOPID controller is designed, including in feedforward and feedback controller, fractional calculus and approximation of fractional operator. Meanwhile, the parameters of controller are analyzed. Based on them, a BP neural network is built to adjust the parameters in this control structure according to the error between the reference trajectory and the actual output. Finally, the proposed control approach is conducted in atomic force microscope tracking control experiment, experimental results verify the effectiveness and improvement of the proposed control approach.

Cite this article as:
S. Chang, C. Peng, S. Dai, and J. Wang, “2-DOF Fractional Order PID Control Based on BP Neural Network for Atomic Force Microscope,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.6, pp. 944-951, 2022.
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Last updated on Nov. 24, 2022