Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control

Hongliang Gao, Xiaoling Li, Chao Gao, Jie Wu and Shiping Wen

Discrete Dynamics in Nature and Society, 2021, vol. 2021, 1-14

Abstract: This paper presents several control methods and realizes the stable tracking for the inverted pendulum system. Based on the advantages of RBF and traditional PID, a novel PID controller based on the RBF neural network supervision control method (PID-RBF) is proposed. This method realizes the adaptive adjustment of the stable tracking signal of the system. Furthermore, an improved PID controller based on RBF neural network supervision control strategy (IPID-RBF) is presented. This control strategy adopts the supervision control method of feed-forward and feedback. The response speed of the system is further improved, and the overshoot of the tracking signal is further reduced. The tracking control simulation of the inverted pendulum system under three different signals is given to illustrate the effectiveness of the proposed method.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:5536573

DOI: 10.1155/2021/5536573

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