Adaptive neuro-fuzzy inference control for active stabilizer bars based on multiple data sources.
1Thuyloi University, Hanoi, Vietnam.
This study introduces an intelligent control system using adaptive neuro-fuzzy inference system (ANFIS) to enhance vehicle stability. The ANFIS-controlled active stabilizer bars significantly reduce rollover risk and improve wheel-road interaction during steering maneuvers.
Area of Science:
- Automotive Engineering
- Control Systems
- Artificial Intelligence
Background:
- Vehicle rollover is a critical safety concern during steering maneuvers.
- Traditional anti-roll systems have limitations in actively managing vehicle dynamics.
- Intelligent control offers potential for advanced stability enhancement.
Purpose of the Study:
- To develop and evaluate an adaptive neuro-fuzzy inference system (ANFIS) for controlling active stabilizer bars.
- To demonstrate the superiority of ANFIS over PID and passive systems in preventing vehicle rollover.
- To improve vehicle stability and wheel-road interaction during dynamic steering conditions.
Main Methods:
- Implementation of an ANFIS algorithm to control active anti-roll systems.
- Training the ANFIS using carefully selected data from previous studies.
- Comparative simulation analysis against Proportional-Integral-Derivative (PID) and passive (mechanical) systems.
Main Results:
- ANFIS significantly reduced the roll angle from 8.15° to 6.87°, outperforming PID (7.08°) and passive (7.80°) systems.
- ANFIS control increased vertical wheel force from 671.06 N to 3030.40 N, enhancing stability.
- Rollover was prevented at high speeds (80-90 km/h) with ANFIS, unlike systems without active bars.
Conclusions:
- ANFIS provides a highly effective intelligent control solution for active anti-roll systems.
- The proposed ANFIS controller ensures robust vehicle rolling stability across various driving conditions.
- Active stabilizer bars managed by ANFIS offer superior performance in preventing rollover and maintaining tire contact.
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