Open AccessNeural Network Supervision Control Strategy for Inverted Pendulum Tracking Control
Author(s) -
Hongliang Gao,
Xiaoling Li,
Chao Gao,
Jie Wu
Publication year - 2021
Publication title -
discrete dynamics in nature and society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 39
eISSN - 1607-887X
pISSN - 1026-0226
DOI - 10.1155/2021/5536573
Subject(s) - inverted pendulum , pid controller , control theory (sociology) , overshoot (microwave communication) , computer science , artificial neural network , tracking (education) , signal (programming language) , controller (irrigation) , control signal , control system , control engineering , control (management) , artificial intelligence , nonlinear system , engineering , temperature control , physics , psychology , telecommunications , pedagogy , agronomy , electrical engineering , quantum mechanics , biology , programming language
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.
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