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Variable-Weighting Linear Quadratic Regulator Control Strategy for Active Suspension Systems
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Jinyun Lü, Yuying Qin, Junfeng Du, Jun Wang
Automotive Engineer | 2026, (6) : 23 - 30
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Automotive Engineer | 2026, (6): 23-30
Variable-Weighting Linear Quadratic Regulator Control Strategy for Active Suspension Systems
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Jinyun Lü, Yuying Qin, Junfeng Du, Jun Wang
Affiliations
  • Liaoning University of Technology, Jinzhou 121001
Published: 2026-06-15 doi: 10.20104/j.cnki.1674-6546.20250263
Outline
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Traditional Linear Quadratic Regulator (LQR) control in vehicle active suspension systems faces challenges in precisely directing optimization due to fixed weight coefficients, which fail to meet the demands of complex operating conditions. To address this issue, a permanent magnet linear motor actuator model, a seven-degree-of-freedom active suspension model and state-space equations are established. Multiple operating modes are defined according to different road profiles and vehicle speeds. The Analytic Hierarchy Process (AHP) is employed to determine weighted coefficients for the fitness function, and the Red-billed Blue Magpie Optimization (RBMO) algorithm is utilized to optimize the LQR weight coefficient matrices. Simulations under various operating conditions are conducted using MATLAB/Simulink. The results indicate that, compared with a passive suspension system, the proposed strategy improves vertical body acceleration, pitch angular acceleration and the dynamic deflection of the right front, left front, left rear, and right rear suspensions in comfort mode by 27%, 30%, 17%, 14%, 8%, and 15%, respectively. In safety mode, roll angular acceleration and the dynamic loads of the right front, left front, left rear, and right rear tires are improved by 41%, 15%, 18%, 15%, and 17%, respectively. In integrated mode, body acceleration pitch angular acceleration and roll angular acceleration are improved by 35%, 32%, and 48%, respectively. Further validation through CarSim/Simulink co-simulation demonstrates that the proposed control strategy achieves a notable level of performance improvement across different modes.

Red-billed Blue Magpie Optimizer (RBMO)  /  Linear Quadratic Regulator (LQR)  /  Active suspension  /  Multimodal  /  Variable weight coefficients  /  Fitness function
吕金运, 秦玉英, 杜俊风, 王军. 基于变权重线性二次型调节器的主动悬架控制. 汽车工程师, 2026 , (6) : 23 -30 . DOI: 10.20104/j.cnki.1674-6546.20250263
Jinyun Lü, Yuying Qin, Junfeng Du, Jun Wang. Variable-Weighting Linear Quadratic Regulator Control Strategy for Active Suspension Systems[J]. Automotive Engineer, 2026 , (6) : 23 -30 . DOI: 10.20104/j.cnki.1674-6546.20250263
Year 2026 volume Issue 6
171
44
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doi: 10.20104/j.cnki.1674-6546.20250263
  • Online Date:2026-09-20
  • Published:2026-06-15
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  • Revised:2025-12-18
Affiliations
    Liaoning University of Technology, Jinzhou 121001
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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