Taking a single magnet levitation system as theobject, a nonlinear numerical model of the vehicle–guidewaycoupling system was established to study the levitationcontrol strategies. According to the similarity in dyna...Taking a single magnet levitation system as theobject, a nonlinear numerical model of the vehicle–guidewaycoupling system was established to study the levitationcontrol strategies. According to the similarity in dynamics,the single magnet-guideway coupling system was simplifiedinto a magnet-suspended track system, and the correspondinghardware-in-loop test rig was set up usingdSPACE. A full-state-feedback controller was developedusing the levitation gap signal and the current signal, andcontroller parameters were optimized by particle swarmalgorithm. The results from the simulation and the test rigshow that, the proposed control method can keep the systemstable by calculating the controller output with the fullstateinformation of the coupling system, Step responsesfrom the test rig show that the controller can stabilize thesystem within 0.15 s with a 2 % overshot, and performswell even in the condition of violent external disturbances.Unlike the linear quadratic optimal method, the particleswarm algorithm carries out the optimization with thenonlinear controlled object included, and its optimizedresults make the system responses much better.展开更多
The high-speed maglev vehicle/guideway coupled model is an essential simulation tool for investigating vehicle dynamics and mitigating coupled vibration.To improve its accuracy efficiently,this study investigated a hi...The high-speed maglev vehicle/guideway coupled model is an essential simulation tool for investigating vehicle dynamics and mitigating coupled vibration.To improve its accuracy efficiently,this study investigated a hierarchical model updating method integrated with field measurements.First,a high-speed maglev vehicle/guideway coupled model,taking into account the real effect of guideway material properties and elastic restraint of bearings,was developed by integrating the finite element method,multi-body dynamics,and electromagnetic levitation control.Subsequently,simultaneous in-site measurements of the vehicle/guideway were conducted on a high-speed maglev test line to analyze the system response and structural modal parameters.During the hierarchical updating,an Elman neural network with the optimal Latin hypercube sampling method was used to substitute the FE guideway model,thus improving the computational efficiency.The multi-objective particle swarm optimization algorithm with the gray relational projection method was applied to hierarchically update the parameters of the guideway layer and magnetic force layer based on the measured modal parameters and the electromagnet vibration,respectively.Finally,the updated coupled model was compared with the field measurements,and the results demonstrated the model’s accuracy in simulating the actual dynamic response,validating the effectiveness of the updating method.展开更多
文摘Taking a single magnet levitation system as theobject, a nonlinear numerical model of the vehicle–guidewaycoupling system was established to study the levitationcontrol strategies. According to the similarity in dynamics,the single magnet-guideway coupling system was simplifiedinto a magnet-suspended track system, and the correspondinghardware-in-loop test rig was set up usingdSPACE. A full-state-feedback controller was developedusing the levitation gap signal and the current signal, andcontroller parameters were optimized by particle swarmalgorithm. The results from the simulation and the test rigshow that, the proposed control method can keep the systemstable by calculating the controller output with the fullstateinformation of the coupling system, Step responsesfrom the test rig show that the controller can stabilize thesystem within 0.15 s with a 2 % overshot, and performswell even in the condition of violent external disturbances.Unlike the linear quadratic optimal method, the particleswarm algorithm carries out the optimization with thenonlinear controlled object included, and its optimizedresults make the system responses much better.
基金The study described in this paper was supported by the National Key Research and Development Program of China(No.2016YFB1200602-30).
文摘The high-speed maglev vehicle/guideway coupled model is an essential simulation tool for investigating vehicle dynamics and mitigating coupled vibration.To improve its accuracy efficiently,this study investigated a hierarchical model updating method integrated with field measurements.First,a high-speed maglev vehicle/guideway coupled model,taking into account the real effect of guideway material properties and elastic restraint of bearings,was developed by integrating the finite element method,multi-body dynamics,and electromagnetic levitation control.Subsequently,simultaneous in-site measurements of the vehicle/guideway were conducted on a high-speed maglev test line to analyze the system response and structural modal parameters.During the hierarchical updating,an Elman neural network with the optimal Latin hypercube sampling method was used to substitute the FE guideway model,thus improving the computational efficiency.The multi-objective particle swarm optimization algorithm with the gray relational projection method was applied to hierarchically update the parameters of the guideway layer and magnetic force layer based on the measured modal parameters and the electromagnet vibration,respectively.Finally,the updated coupled model was compared with the field measurements,and the results demonstrated the model’s accuracy in simulating the actual dynamic response,validating the effectiveness of the updating method.
