Learning Control Parameter for Transmissions Using Hierarchical Stochastic Optimization

-Simulation-based Parameter Selection-

Abstract

In many mechanical systems, control parameters are tuned by a human expert through trial and error, which is labor-intensive and time-consuming. For example, electronically controlled transmissions (ECT) require such parameter optimization. To address this issue, we propose a parameter optimization system for ECT by using Hierarchical Stochastic Optimization (HSO) that is able to handle multimodal objective function. The optimizer learns better parameters which show high peformance in all metrics and satisfy all constraints. In the experiments, we use multi-physics simulators and optimize the parameters for ECT. Through experiments, we demonstrate that our HSO can identify several modes of the objective function and is more sample-efficient than random search and a human operator.