Utilizing Hybrid Machine Learning Framework for Half-Vehicle Suspension Control to Minimize Road-Induced Vibrations.

In this paper, hybrid feed-forward deep neural network and ANFIS framework is developed to control an active suspension system of half vehicle. The dataset were generated from previous literature that studies driver comfort on different road profiles. The deep neural network aims to learn intricate relationships within dataset between features and output. The deep neural network was trained using back propagation algorithm and automated search method was implemented to obtain optimum network structure. The paper starts generating various road roughness profiles according to ISO 8608. Then, through comprehensive examination of rear and front body displacements and pitch angle accelerations, the study highlights system's significant contributions to ride comfort and vehicle dynamics. The proposed framework outperforms other controllers like proportional-integral-derivative (PID), demonstrating its robustness across different road profiles. The results demonstrated effectiveness of proposed control to minimize peak overshooting and settling times which improves ride comfort and stability significantly. Also, the proposed model has small root mean square error (RMSE) values which indicate smoother and less energetic responses, which are typically preferred for passenger comfort. Furthermore, the adaptive neural fuzzy inference system-deep neural network (ANFIS-DNN) has minimum crest factor (CF) which indicates that the signal has fewer peaks relative to its average. [ABSTRACT FROM AUTHOR]