Result: Quantitative Performance Analysis of Spring-Mass-Damper Control Systems: A Comparative Implementation in Python and R

Title:
Quantitative Performance Analysis of Spring-Mass-Damper Control Systems: A Comparative Implementation in Python and R
Source:
Indonesian Journal of Applied Mathematics; Vol. 5 No. 1 (2025): Indonesian Journal of Applied Mathematics Vol. 5 No. 1 April Chapter; 10-26 ; 2774-2016 ; 2774-2067 ; 10.35472/indojam.v5i1
Publisher Information:
Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia
Publication Year:
2025
Collection:
Journal of Science and Application Technology (JSAT - Institut Teknologi Sumatera)
Document Type:
Academic journal article in journal/newspaper
File Description:
application/pdf
Language:
English
DOI:
10.35472/indojam.v5i1.2104
Rights:
Copyright (c) 2025 Indonesian Journal of Applied Mathematics ; https://creativecommons.org/licenses/by-nc/4.0
Accession Number:
edsbas.9D651111
Database:
BASE

Further Information

The numerical simulation of control of spring-mass-damper (SMD) systems offer critical insights into dynamical systems and computational methodologies. This study provides a comprehensive comparative analysis of implementing SMD systems across two prominent open-source scientific computing platforms: Python and R. By examining both open-loop and closed-loop system configurations, the research investigates the computational performance, numerical accuracy, and implementation characteristics of these platforms. Utilizing an idealized one-dimensional SMD system with a Proportional-Integral-Derivative (PID) controller, the study conducted extensive numerical simulations and statistical performance analyses. Results revealed Python's significant advantages in execution speed, achieving up to 63.57% reduction in runtime for controlled system simulations, while R demonstrated superior consistency in execution and memory usage. The controlled system demonstrated exceptional performance, with a final position error of merely 0.4% and enhanced damping characteristics. This work not only bridges theoretical stability analysis with empirical performance insights but also promotes reproducibility and transparency in computational dynamics research by leveraging open-source platforms.