Semi-automated transmission control for motorcycle gearshift: design, data-driven tuning and experimental validation

TL;DR

This paper presents a data-driven method using Constrained Bayesian Optimization to improve semi-automated gearshift control in motorcycles, achieving fast, smooth shifts with minimal modifications and validated through real-world experiments.

Contribution

It introduces a novel data-driven tuning approach for motorcycle gearshift control using Constrained Bayesian Optimization, enhancing performance and calibration precision.

Findings

Optimized gearshift parameters improve shift smoothness and speed.

The method demonstrates fast convergence and repeatability in real vehicle tests.

Validated approach enhances semi-automated transmission performance.

Abstract

This brief addresses the gearshifting problem for Semi-Automated Manual Transmissions (S-AMT) in powered two-wheelers, a powertrain setup that allows fast and smooth gear shifts with minimal modifications to the traditional manual powertrain layout. We show that with a proper synchronization between the electronic clutch and engine torque, excellent gearshift performance can be obtained, but requires precise parameters calibration. We thus propose the use of a data-driven approach with Constrained Bayesian Optimization to optimize control parameters. The procedure's effectiveness is demonstrated on a real vehicle, assessing performance in terms of optimality, convergence rate, and repeatability.