Open AccessSelf-Adaptive PID Control Based on RBF Network for Trajectory Tracking of Dual-Mass Servo System
Author(s) -
Weiwen Hu,
Shengguo Zhang,
Ning Lü
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1871/1/012113
Subject(s) - pid controller , control theory (sociology) , servomechanism , robustness (evolution) , computer science , artificial neural network , trajectory , servo , control engineering , tracking (education) , servo control , servomotor , engineering , artificial intelligence , control (management) , physics , temperature control , psychology , pedagogy , biochemistry , chemistry , astronomy , gene
This paper presents a state-of-the-art algorithm of self-adaptive RBF network PID control and aims at achieving the precise and fast trajectory tracking for a dual-mass servo system. To increase the robustness of servo system parameter varying and external disturbances, a classical PID algorithm with enhanced structure based on RBF neural network is proposed. Extensive simulations show that it practically validates the superiority of the proposed RBF adaptive PID controller. The experiment was simulated respectively under the external disturbance, which indicates that accurate tracking performance of the servo system with dual-mass load has been achieved and also verifies the effectiveness of self-adaptive PID control strategy.