GA-LQR control for multi-objective optimization in inerter-based active suspension

Inerter-based active suspension systems have great potential for improving vehicle dynamic performance, but their controller design is still challenged by the conflicting requirements of ride comfort, handling stability, and control energy consumption. Conventional linear quadratic regulator control usually depends on empirical selection of weighting matrices and does not explicitly consider control power, which makes it difficult to achieve a satisfactory balance among body acceleration, suspension deflection, dynamic tire load, and actuator energy consumption. To address this issue, a quarter-car model equipped with an inerter element is established, and a Genetic Algorithm–Linear Quadratic Regulator (GA-LQR) control framework is developed for the active suspension system. In this method, the logarithmic parameters of the Linear Quadratic Regulator (LQR) weighting matrices are globally optimized by a genetic algorithm, so that body acceleration, suspension deflection, dynamic tire load, and control energy consumption can be considered simultaneously in the controller design. In this way, the traditional trial-and-error tuning of LQR weights is transformed into a systematic optimization process for multi-objective suspension control. Simulation results under random road excitation show that the proposed GA-LQR strategy provides better overall performance than both passive suspension and conventional LQR-controlled inerter suspension. Compared with standard LQR control, the proposed method reduces body acceleration by 39.01% and suspension deflection by 43.1%, while the corresponding reductions achieved by conventional LQR are 12.75% and 7.4%, respectively. In addition, tire-road contact stability is further improved. The results demonstrate that the GA-optimized weighting matrices can effectively improve the comprehensive performance of inerter-based active suspension systems and provide an effective control approach for vibration reduction with lower energy cost.

Authors

Institutions

Publication Details

Journal
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Published
2026-06-28
DOI
https://doi.org/10.1177/09544070261462192
Primary Topic
Vibration Control and Rheological Fluids
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

GA-LQR control for multi-objective optimization in inerter-based active suspension

Dongxing Cao, 常中坤, Yi-Xiang Wang, Haoxiang Zang et al.
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Vibration Control and Rheological Fluids
article

GA-LQR control for multi-objective optimization in inerter-based active suspension

Dongxing Cao, 常中坤, Yi-Xiang Wang, Haoxiang Zang, XinXing Zhou
article en

Abstract

Inerter-based active suspension systems have great potential for improving vehicle dynamic performance, but their controller design is still challenged by the conflicting requirements of ride comfort, handling stability, and control energy consumption. Conventional linear quadratic regulator control usually depends on empirical selection of weighting matrices and does not explicitly consider control power, which makes it difficult to achieve a satisfactory balance among body acceleration, suspension deflection, dynamic tire load, and actuator energy consumption. To address this issue, a quarter-car model equipped with an inerter element is established, and a Genetic Algorithm–Linear Quadratic Regulator (GA-LQR) control framework is developed for the active suspension system. In this method, the logarithmic parameters of the Linear Quadratic Regulator (LQR) weighting matrices are globally optimized by a genetic algorithm, so that body acceleration, suspension deflection, dynamic tire load, and control energy consumption can be considered simultaneously in the controller design. In this way, the traditional trial-and-error tuning of LQR weights is transformed into a systematic optimization process for multi-objective suspension control. Simulation results under random road excitation show that the proposed GA-LQR strategy provides better overall performance than both passive suspension and conventional LQR-controlled inerter suspension. Compared with standard LQR control, the proposed method reduces body acceleration by 39.01% and suspension deflection by 43.1%, while the corresponding reductions achieved by conventional LQR are 12.75% and 7.4%, respectively. In addition, tire-road contact stability is further improved. The results demonstrate that the GA-optimized weighting matrices can effectively improve the comprehensive performance of inerter-based active suspension systems and provide an effective control approach for vibration reduction with lower energy cost.

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Beijing University of Technology (CN), Beijing Academy of Artificial Intelligence (CN), University of Science and Technology Beijing (CN)
Affordable and clean energy
Openalex Percentile: Top 11%
Vibration Control and Rheological Fluids
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.