Optimal fuzzy robust state feedback control for a five DOF active suspension system

Active suspension systems are integral to modern vehicles, enhancing driving comfort by addressing road irregularities and isolating the vehicle's interior from vibrations. In this research, we construct an active suspension system with five degrees of freedom (DOF) and find the best fuzzy robust state feedback controller to control it. While designing the state feedback controller, we considered the initial errors in the relative displacement and acceleration as well as their derivatives. A singleton fuzzifier, center average russification, and product inference engine are all control parameters managed by a fuzzy system. Optimization using the Sine Cosine Algorithm (SCA) is then used to determine the optimal gains for the controller that has been constructed. The technique uses two objective functions for depreciation: the body's acceleration, the relative displacement between the tire and sprung mass. Results show that the suggested active suspension system is better than that of previous studies.

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Publication Details

Journal
Results in Control and Optimization
Published
2024-11-24
DOI
https://doi.org/10.1016/j.rico.2024.100504
Citations
6
Primary Topic
Vibration Control and Rheological Fluids
Type
article
Field-Weighted Citation Impact
1.57
Controls
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article

Optimal fuzzy robust state feedback control for a five DOF active suspension system

M. J. Mahmoodabadi, N. Nejadkourki, M. Yousef Ibrahim
6 citations
Results in Control and Optimization
Vibration Control and Rheological Fluids
1.57
article

Optimal fuzzy robust state feedback control for a five DOF active suspension system

M. J. Mahmoodabadi, N. Nejadkourki, M. Yousef Ibrahim
article en
6 citations

Abstract

Active suspension systems are integral to modern vehicles, enhancing driving comfort by addressing road irregularities and isolating the vehicle's interior from vibrations. In this research, we construct an active suspension system with five degrees of freedom (DOF) and find the best fuzzy robust state feedback controller to control it. While designing the state feedback controller, we considered the initial errors in the relative displacement and acceleration as well as their derivatives. A singleton fuzzifier, center average russification, and product inference engine are all control parameters managed by a fuzzy system. Optimization using the Sine Cosine Algorithm (SCA) is then used to determine the optimal gains for the controller that has been constructed. The technique uses two objective functions for depreciation: the body's acceleration, the relative displacement between the tire and sprung mass. Results show that the suggested active suspension system is better than that of previous studies.

Results in Control and OptimizationVol. 17
Federation University (AU), Sirjan University of Technology (IR)
Affordable and clean energy
Openalex Percentile: Top 18%
Vibration Control and Rheological Fluids
1.57
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Optimal fuzzy robust state feedback control for a five DOF active suspension system — M. J. Mahmoodabadi, N. Nejadkourki, et al. · Results in Control and Optimization (2024) | TGRS Research Map | TGRS