Affiliation:
1. School of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu, China
2. School of Engineering, University of Bridgeport, Bridgeport, CT, USA
3. School of Mechanical Engineering, Anhui University of Technology, Ma’anshan, China
Abstract
In order to enhance automotive ride performance, permanent magnets and magnetic valves were introduced in magnetorheological dampers and hence permanent magnets and magnetic valves introduced in magnetorheological damper’s air suspension was studied. First, on the basic principle of providing magnetic field by permanent magnets and adjusting damping force by magnetic valves, a novel permanent magnets and magnetic valves introduced in magnetorheological damper was designed. On the basis of equivalent surface current model, magnetic induction intensity activated by permanent magnets in damping channel was calculated, and damping force model was built based on hydromechanics theory. Damping force–displacement experiments and damping force–velocity experiments were carried out, and the theoretical model of permanent magnets and magnetic valves introduced in magnetorheological damper was verified. Then, physical and mathematical models of 4 degree-of-freedom permanent magnets and magnetic valves introduced in magnetorheological damper air suspension model were built. In order to enhance the controlling effects of the studied air suspension, optional moving operation, moving variation operation and operation of step size changing with fluorescein proportionality were introduced into glowworm swarm optimization algorithm, and glowworm swarm optimization proportional–integral–derivative controlling algorithm was designed. Finally, three experiments with input of impact road surface, sine wave road surface, and white noise road surface were carried out, and the experimental results verified that the working performance of permanent magnets and magnetic valves introduced in magnetorheological damper air suspension based on glowworm swarm optimization proportional–integral–derivative controlling algorithm was good.
Funder
National Natural Science Foundation of China
Cited by
6 articles.
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