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
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.
