基于模糊推理和Jordan神经网络的磁悬浮球位置补偿控制研究

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中图分类号：TP273.1 文献标志码：A

Position compensation control of maglev ball system by fuzz inference and Jordan neural network

LI Xiaoru， CHEN Shisong， HUANG Zhiwen （SchoolofMechanical Engineering， UniversityofShanghai for Scienceand TechnologyShanghai 2ooo93，China）

Abstract： To address the issue of poor dynamic performance in control systems caused by the output uncertainty of insufficiently trained Jordan neural network （JNN），a novel position compensation control method of maglev ball based on fuzzy inference （FI） and JNN was proposed. A three-module control framework was designed， consisting of a basic control module， a JNN control module， and a FI module. The basic control module adopted a highly adaptable PID controler to provide baseline control performance. The JNN control module performed real-time identification and compensation for the maglev bal system. The FI module dynamically adjusted the output of the neural network controller to suppress the uncertainty introduced by insufficiently trained JNN. The experimental results show that， compared with the traditional neural network compensation control method， the proposed method reduces overshoot by 39.79% and 60.61% and shortens the settling time by 19.52% and 48.47% when tracking step and square wave signals. The proposed method significantly enhances the dynamic performance of the control system while maintaining steady-state accuracy.

Keywords： fuzzy inference; Jordan neural network; position compensation control; maglev ball

近年来，具有无摩擦、低噪声、少污染等特点的磁悬浮技术逐渐应用于多个领域，如磁悬浮车辆、磁悬浮电机、磁悬浮轴承和磁悬浮球[1]等。（剩余13362字）