Abstract
Hybrid suspension system with permanent magnet and electromagnet consumes little power consumption and can realize larger suspension gap. But realizing stable suspension of hybrid magnet is a tricky problem in the suspension control sphere. Considering from this point, we take magnetic flux signal as a state variable and put this signal back to suspension control system. So we can get the hybrid suspension mathematical model based on magnetic flux signal feedback. By application of MIMO feedback linearization theory, we can further realize linearization of the hybrid suspension system. And then proportion, integral, differentiation, magnetic flux density B (PIDB) controller is designed. Some hybrid suspension experiments have been done on CMS04 magnetic suspension bogie of National University of Defense Technology (NUDT) in China. The experiments denote that the new hybrid suspension control algorithm based on magnetic flux signal feedback designed in this paper has more advantages than traditional position-current double cascade control algorithm. Obviously, the robustness and stability of hybrid suspension system have been enhanced.
Highlights
A hybrid electromagnet made of permanent magnet and electromagnet consumes little power consumption and can realize larger suspension gap
Some hybrid suspension experiments have been done on CMS04 magnetic suspension bogie of National University of Defense Technology (NUDT) in China
The hybrid suspension system after linearization is controlled completely, so we can regulate the property of hybrid suspension system by designing PIDB suspension controller
Summary
A hybrid electromagnet made of permanent magnet and electromagnet consumes little power consumption and can realize larger suspension gap. Literature [1] founded the hybrid suspension model based on current feedback. Literature [3] mainly designs a robust fuzzy-neural-network control (RFNNC) scheme for the levitated positioning of the linear maglev rail system with nonnegative inputs. Wai and Lee [7, 8] has designed an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating a sliding-mode control (SMC) strategy for a magneticlevitation (maglev) transportation system. Based on sliding mode control with the feedback linearization, a kind of nonlinear control strategy of electrical Maglev air gap was offered the design method of the system was researched [9]. The control method based on magnetic flux signal feedback has a bright prospect
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