Adaptive Artificial Neural Network Surrogate Model of Nonlinear Hydraulic Adjustable Damper for Automotive Semi-Active Suspension System

Artificial neural network surrogate models are often used in the design optimization of the automotive semi-active suspension system. To realize the desired damping force, a surrogate model needs to be constructed to approximate the regulating mechanism of the hydraulic adjustable damper. However, very few of the existing studies discuss how to guarantee the modeling accuracy. To this end, this work constructs a novel surrogate model by using radial basis function neural network. Meanwhile, an adaptive modeling method based on modified hyperband and trust-region-based mode pursuing sampling is presented. Concretely, modified hyperband is used to fast select a seed model by early-stopping and dynamic resource allocation. Mode pursuing sampling is then performed in the neighborhood of the seed model, to systematically generate more sample points while statistically covering the entire neighborhood (i.e., trust region). In particular, in the mode pursuing sampling procedure, quadratic regression is performed around the current optimum as the second detection. Moreover, as the position or size of the trust region changes, the sampling and detection process iterate until the accuracy of the model is no longer improved. To avoid falling into the local optimum, the seed model selection and mode pursuing sampling process iterate until the termination criterion is met. The experimental results show that compared with the benchmarks, the modeling accuracy of hydraulic adjustable damper can be improved by up to 48%, and the iteration resources can be reduced by up to 84%.