Investigating Performance of Quarter-Car Semi-Active Suspension with Skyhook, Fuzzy Logic, Adaptive Neuro-Fuzzy Inference System Control Strategies for ISO-Classified Road Disturbance 2020-01-5040
In this paper, a quarter-car suspension system has been investigated for the International Organization for Standardization (ISO)-classified road profile with various control strategies. The vehicle suspension system provides ride comfort and handling by reducing the transfer of road disturbances or irregular road profile to the passenger and cargo materials. The suspension also retains the road and tire contact, stabilizing the vehicle’s movements. A combination of fuzzy logic and neural network, i.e., adaptive neuro-fuzzy inference system (ANFIS), is deployed as a control strategy to control the quarter-car semi-active suspension model. Quarter-car suspension models with a passive control and semi-active controller with different control strategies, viz., Skyhook, Fuzzy Logic (FLC), and ANFIS, are designed and modeled in MATLAB/SIMULINK®. Numerical simulations were performed on developed quarter-car models for an ISO-classified road profile disturbance, and the performance was compared. With respect to the passive suspension system, there is better ride comfort performance (9.7%) with skyhook control, but a compromise in handling, while FLC achieves both ride comfort (4%) and handling (6.12%), reducing the trade-off between both performances. This suspension performance is better achieved by the ANFIS ride comfort (54.57%) and handling (20.57%) with respect to (wrt) the passive suspension system. The comparative implementation of the above control strategies concludes better suspension by the ANFIS of the vehicle to the ISO-classified road disturbance than those other control strategies.
Citation: Mulla, A. and Unune, D., "Investigating Performance of Quarter-Car Semi-Active Suspension with Skyhook, Fuzzy Logic, Adaptive Neuro-Fuzzy Inference System Control Strategies for ISO-Classified Road Disturbance," SAE Technical Paper 2020-01-5040, 2020, https://doi.org/10.4271/2020-01-5040. Download Citation