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%. INDEX TERMS semi-active suspension systems, hydraulic adjustable damper, artificial neural network, hyperparameter optimization.
Simulation and optimization methods have been widely used in forklift design due to their cost-effectiveness. However, this type of method involves challenges such as the accuracy of the simulation model and the simulation solution time. These challenges reduce the stability and precision of the surrogate model and hence generate further optimization errors. In this paper, a multi-objective surrogate modeling (MSM) method for telescopic boom forklifts based on closed-loop transfer learning is proposed in order to solve these challenges. The MSM consists of the following two steps: to pre-train an initial deep neural network model (deep model) with a large amount of existing simulation data from the same type of forklift and to transfer the model with a small amount of measurement data collected on the current forklift. A general framework for deep neural network (DNN) training is introduced to improve the approximation ability of the initial model. Moreover, a novel uncertainty-analysis-based sampling method is suggested for measurement data development, and combined with transfer learning to form a closed-loop mode to improve the stability of the final model. The superiority of MSM is demonstrated through comparative studies with the fine-tuning method on a telescopic boom forklift with two objectives. The experimental results show that the Correlation coefficient (R) of the deep model can reach 0.9971 by using only 80 sets of training data. In addition, it can also achieve an improvement of at least a 13.25% reduction in Root Mean Squared Error (RMSE) and a 9.19% reduction on average in Maximum Absolute Error (MAE), as well as stronger robustness compared to the benchmarks. Furthermore, it will provide a valuable reference for the simulation optimization of complex electromechanical products.INDEX TERMS Telescopic boom forklift, surrogate model, transfer learning, uncertainty analysis, hyperparameter optimization.
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