Increased Dynamic Drivetrain Performance by Implementing a Modular Design with Decentralized Control Architecture

Author:

Divens Niels1ORCID,Tuerlinckx Théo1ORCID,Westerhof Bernhard1,Stockman Kurt23ORCID,van Os David23ORCID,Laurijssen Koen1ORCID

Affiliation:

1. MotionS Core Lab, Flanders Make vzw, 3001 Leuven, Belgium

2. Department of Electromechanical, Systems and Metal Engineering, Ghent University, 9052 Gent, Belgium

3. MIRO Core Lab, Flanders Make vzw, 9052 Gent, Belgium

Abstract

This paper assesses the energy consumption, control performance, and application-specific functional requirements of a modular drivetrain in comparison to a benchmark drivetrain. A decentralised control architecture has been developed and validated using mechanical plant models. Simscape models have been validated with data from an experimental setup including an equivalent modular and benchmark drivetrain. In addition, the control strategy has been implemented and validated on the experimental setup. The results prove the ability of the control strategy to synchronize the motion of the different sliders, resulting in crank position tracking errors below 0.032 radians on the setup. The model and experimental data show an increased performance of the modular drivetrain compared to the benchmark drivetrain in terms of energy consumption, control performance, and functional requirements. The modular drivetrain is especially advantageous for machines running highly dynamic motion profiles due to the reduced inertia. For such motion profiles, an increased position tracking of up to 84% has been measured. In addition, it is shown that the modular drivetrain root mean square (RMS) torque is reduced with 32% compared to the benchmark drivetrain. However, these mechanical energy savings are partly counteracted by the higher motor losses seen in the modular drivetrain, resulting in potential electrical energy savings of around 29%.

Funder

Flanders Make vzw

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Optimization,Mechanical Engineering,Computer Science (miscellaneous),Control and Systems Engineering

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