Abstract
Control of the maglev system is one of the most significant technologies of the maglev train. The common proportion integration differentiation (PID) method, which has fixed control parameters, ignores the non-linearity and uncertainty of the model in the design process. In the actual process, due to environmental changes and interference, the inherent parameters of the system will drift significantly. The traditional PID controller has difficulty meeting the control requirements, and will have poor control effect in the actual working environment. Therefore, a radial basis function (RBF)-PID controller is designed in this article, which can use the information from the levitation system identified by the RBF network to adjust the parameters of the controller in real time. Compared with the traditional PID control method, it is shown that the RBF-PID method can improve the control performance of the system through simulation and experiment.
Highlights
As a new type of transportation with many advantages such as little noise and vibration, less abrasion and environmental dependence, the maglev train will play a crucial role in future traffic systems
Anon and Suebsran [9] proposed an adaptive neural network control structure, which used the radial basis function (RBF) network to approximate the non-linear links in the magnetic levitation system in electromagnetic suspension (EMS)
In the online adjustment of proportion integration differentiation (PID) parameters according to the RBF network, the parameter of the current loop kC can be fixed at an appropriate value, which can reduce the difficulty of controller design
Summary
As a new type of transportation with many advantages such as little noise and vibration, less abrasion and environmental dependence, the maglev train will play a crucial role in future traffic systems. Ishtiaq Ahmad presented the design of the PID controller for a magnetic levitation system based on an efficient method of tuning controller parameters using the genetic algorithm [3]. Anon and Suebsran [9] proposed an adaptive neural network control structure, which used the radial basis function (RBF) network to approximate the non-linear links in the magnetic levitation system in electromagnetic suspension (EMS). Introduced a non-contact inductive gap sensor method for compensating high-speed maglev trains based on the RBF network. This scheme can accurately estimate the correct air gap distance in a wide temperature range. We hope the research can promote the combination of intelligent control technology and levitation control technology
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