Integrating digital twins with neural networks for adaptive control of automotive suspension systems.

This paper presents an innovative approach to enhancing the adaptive control of automotive suspension systems by integrating digital twin (DT) technology with artificial neural networks (ANNs). The proposed method leverages real-time data from DTs to dynamically adjust the suspension settings, optimizing ride comfort and vehicle handling. A detailed simulation model of a vehicle's suspension system was developed using MATLAB/Simulink, with the DT providing continuous feedback to the ANN-based adaptive controller. The effectiveness of the proposed method was evaluated through a series of simulations under various road conditions and driving scenarios. Results show that the integrated DT and ANN approach improves ride comfort by 8.46% compared to traditional Proportional-Integral-Derivative (PID) control methods, as measured by the reduction in vertical acceleration of the vehicle's body. Additionally, vehicle handling was enhanced by 14.02%, demonstrated by a decrease in the lateral acceleration during cornering. The predictive maintenance capability of the system also showed a 5.72% reduction in suspension component wear, extending the overall lifespan of the system. These findings suggest that the integration of DTs with neural networks (NN) offers significant improvements in both the performance and longevity of automotive suspension systems, providing a compelling case for further development and real-world implementation.