Abstract
This paper presents an intelligent control strategy for an active MacPherson suspension driven by a pneumatic muscle (PM) actuated system and evaluates its improved control performance on a self-developed test-rig. To fulfill the fully-functional test and analysis of active suspension systems, this test-rig comprises three units: 1. an active MacPherson suspension unit, which consists of a PM mechanism and two MacPherson struts, is built to isolate vibration from road disturbances; 2. a road profile generator (RPG) unit, which can produce a vertical force to lift the car-body according to various road profiles; 3. a PC-based control (PBC) unit, which computes, sends and receives signals for both of the active MacPherson suspension and the PC-based control unit. The objective of this study is to improve the MacPherson suspension in terms of the capability of road vibration isolation by using the PM actuation that can actively provide extra compensatory force for MacPherson struts. Then, for motion control of the PM, this study employs an interval type-2 adaptive fuzzy controller to approximate the optimal control law and adopts a self-tuning and fuzzy sliding mode compensator to compensate unmodeled dynamics for the active MacPherson suspension system. Three experiments are conducted to compare the active MacPherson suspension system with the original MacPherson struts through various road profiles on the test-rig. The results show the significant improvement for the proposed active MacPherson suspension system in suppressing the displacement and acceleration of the car-body.
Original language | English |
---|---|
Article number | 8995522 |
Pages (from-to) | 34080-34095 |
Number of pages | 16 |
Journal | IEEE Access |
Volume | 8 |
DOIs | |
Publication status | Published - 2020 |
Keywords
- Active suspension systems
- interval type-2 fuzzy control
- pneumatic muscles
- road vibration suppression
- sliding mode compensator
ASJC Scopus subject areas
- General Computer Science
- General Materials Science
- General Engineering