Modeling and controlling a half-car model suspension system using an ANFIS controller
- 1Department of Motor Vehicle Engineering, Engineering College, Ethiopian Defence University, Bishoftu, Ethiopia
- 2Department of Mechanical Engineering, Center of Excellence for Transport and Vehicle Engineering, Adama Science and Technology University, Adama, Ethiopia
Abstract
Passive suspension systems (PSS) use fixed spring and damping characteristics and cannot adapt to changing road disturbances and operating conditions. Active suspension systems (ASS) address this limitation by generating additional control forces, but controller design must balance ride comfort, road holding, model uncertainty, and control effort. This study develops a four-degree-of-freedom (4-DOF) longitudinal half-car model and evaluates an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller against PSS and PID-controlled ASS under double-bump and ISO-classified random Type A and Type C Road profiles at 10 and 20 m/s. The ANFIS controller is trained using PID-generated input-output data with two inputs, five Gaussian membership functions per input, 25 Takagi-Sugeno rules, and 150 training epochs. For the double-bump case at 10 m/s, ANFIS reduces body vertical displacement from 0.0435 to 0.0084 m (80.6%), vertical acceleration from 2.40 to 0.77 m/s2 (67.9%), and front suspension travel from 0.0652 to 0.0090 m (86.2%) relative to PSS. Compared with PID, the corresponding reductions are 53.6%, 49.3%, and 43.8%, respectively. Settling time is reduced by 38.8% for body vertical displacement and 38.6% for pitch acceleration relative to PSS. The result meets all the qualifications for riding comfort of the ISO 2631 standard. The study identifies limitations related to PID-supervised training, linear modeling, and actuator-energy assessment by employing simulation. Experimental evaluation is required to establish robustness to parameter uncertainty, sensor noise, actuator delay, and implementation energy requirements.
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BibTeX
@article{fisha2026modeling,
title = {{Modeling and controlling a half-car model suspension system using an ANFIS controller}},
author = {Sisay Ewunetie Fisha and Amanuel Gebisa Aga and Tatek Mamo Wachamo},
journal = {PLOS One},
year = {2026},
month = nov,
doi = {10.1371/journal.pone.0361999},
url = {https://doi.org/10.1371/journal.pone.0361999},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Modeling and controlling a half-car model suspension system using an ANFIS controller
AU - Sisay Ewunetie Fisha
AU - Amanuel Gebisa Aga
AU - Tatek Mamo Wachamo
PY - 2026
DA - 2026/11/05
JO - PLOS One
DO - 10.1371/journal.pone.0361999
UR - https://doi.org/10.1371/journal.pone.0361999
AB - Passive suspension systems (PSS) use fixed spring and damping characteristics and cannot adapt to changing road disturbances and operating conditions. Active suspension systems (ASS) address this limitation by generating additional control forces, but controller design must balance ride comfort, road holding, model uncertainty, and control effort. This study develops a four-degree-of-freedom (4-DOF) longitudinal half-car model and evaluates an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller against PSS and PID-controlled ASS under double-bump and ISO-classified random Type A and Type C Road profiles at 10 and 20 m/s. The ANFIS controller is trained using PID-generated input-output data with two inputs, five Gaussian membership functions per input, 25 Takagi-Sugeno rules, and 150 training epochs. For the double-bump case at 10 m/s, ANFIS reduces body vertical displacement from 0.0435 to 0.0084 m (80.6%), vertical acceleration from 2.40 to 0.77 m/s2 (67.9%), and front suspension travel from 0.0652 to 0.0090 m (86.2%) relative to PSS. Compared with PID, the corresponding reductions are 53.6%, 49.3%, and 43.8%, respectively. Settling time is reduced by 38.8% for body vertical displacement and 38.6% for pitch acceleration relative to PSS. The result meets all the qualifications for riding comfort of the ISO 2631 standard. The study identifies limitations related to PID-supervised training, linear modeling, and actuator-energy assessment by employing simulation. Experimental evaluation is required to establish robustness to parameter uncertainty, sensor noise, actuator delay, and implementation energy requirements.
SN - 1932-6203
ER -