Performance Improvement in a Vehicle Suspension System with FLQG and LQG Control Methods
This study investigates the effect of active control on a quarter-vehicle suspension system. The car suspension system was modeled using the Lagrange–Euler method. The linear quadratic Gaussian (LQG) and fuzzy linear quadratic Gaussian (FLQG) control methods were designed and used for active control...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/14/3/137 |
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| author | Tayfun Abut Enver Salkım Andreas Demosthenous |
| author_facet | Tayfun Abut Enver Salkım Andreas Demosthenous |
| author_sort | Tayfun Abut |
| collection | DOAJ |
| description | This study investigates the effect of active control on a quarter-vehicle suspension system. The car suspension system was modeled using the Lagrange–Euler method. The linear quadratic Gaussian (LQG) and fuzzy linear quadratic Gaussian (FLQG) control methods were designed and used for active control to increase vehicle handling and passenger comfort, with the aim of reducing or eliminating vibrations by performing active control of passive suspension systems using these methods. The optimum values of the coefficients of the points where the membership functions of the LQG and Fuzzy LQG methods touch were obtained using the grey wolf optimization (GWO) algorithm. The success of the control performance rate of the applied methods was compared based on the passive suspension system. In addition, the obtained results were compared with each other and with other studies using the integral time-weighted absolute error (ITAE) performance criterion. The proposed control method yielded significant improvements in vehicle parameters compared with the passive suspension system. Vehicle body movement, vehicle acceleration, suspension deflection, and tire deflection improved by approximately 88.2%, 91.5%, 88%, and 89.4%, respectively. Thus, vehicle driving comfort was significantly enhanced based on the proposed system. |
| format | Article |
| id | doaj-art-bdf8092db85c471892f53e5acfc7dc36 |
| institution | Kabale University |
| issn | 2076-0825 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-bdf8092db85c471892f53e5acfc7dc362025-08-20T03:40:41ZengMDPI AGActuators2076-08252025-03-0114313710.3390/act14030137Performance Improvement in a Vehicle Suspension System with FLQG and LQG Control MethodsTayfun Abut0Enver Salkım1Andreas Demosthenous2Department of Mechanical Engineering, Mus Alparslan University, 49250 Muş, TürkiyeDepartment of Electronics and Automation, Mus Alparslan University, 49250 Muş, TürkiyeDepartment of Electronics and Electrical Engineering, University College London (UCL), London WC1E 7JE, UKThis study investigates the effect of active control on a quarter-vehicle suspension system. The car suspension system was modeled using the Lagrange–Euler method. The linear quadratic Gaussian (LQG) and fuzzy linear quadratic Gaussian (FLQG) control methods were designed and used for active control to increase vehicle handling and passenger comfort, with the aim of reducing or eliminating vibrations by performing active control of passive suspension systems using these methods. The optimum values of the coefficients of the points where the membership functions of the LQG and Fuzzy LQG methods touch were obtained using the grey wolf optimization (GWO) algorithm. The success of the control performance rate of the applied methods was compared based on the passive suspension system. In addition, the obtained results were compared with each other and with other studies using the integral time-weighted absolute error (ITAE) performance criterion. The proposed control method yielded significant improvements in vehicle parameters compared with the passive suspension system. Vehicle body movement, vehicle acceleration, suspension deflection, and tire deflection improved by approximately 88.2%, 91.5%, 88%, and 89.4%, respectively. Thus, vehicle driving comfort was significantly enhanced based on the proposed system.https://www.mdpi.com/2076-0825/14/3/137active controllinear quadratic Gaussian (LQG)fuzzy linear quadratic Gaussian (FLQG)grey wolf optimization (GWO) algorithm |
| spellingShingle | Tayfun Abut Enver Salkım Andreas Demosthenous Performance Improvement in a Vehicle Suspension System with FLQG and LQG Control Methods Actuators active control linear quadratic Gaussian (LQG) fuzzy linear quadratic Gaussian (FLQG) grey wolf optimization (GWO) algorithm |
| title | Performance Improvement in a Vehicle Suspension System with FLQG and LQG Control Methods |
| title_full | Performance Improvement in a Vehicle Suspension System with FLQG and LQG Control Methods |
| title_fullStr | Performance Improvement in a Vehicle Suspension System with FLQG and LQG Control Methods |
| title_full_unstemmed | Performance Improvement in a Vehicle Suspension System with FLQG and LQG Control Methods |
| title_short | Performance Improvement in a Vehicle Suspension System with FLQG and LQG Control Methods |
| title_sort | performance improvement in a vehicle suspension system with flqg and lqg control methods |
| topic | active control linear quadratic Gaussian (LQG) fuzzy linear quadratic Gaussian (FLQG) grey wolf optimization (GWO) algorithm |
| url | https://www.mdpi.com/2076-0825/14/3/137 |
| work_keys_str_mv | AT tayfunabut performanceimprovementinavehiclesuspensionsystemwithflqgandlqgcontrolmethods AT enversalkım performanceimprovementinavehiclesuspensionsystemwithflqgandlqgcontrolmethods AT andreasdemosthenous performanceimprovementinavehiclesuspensionsystemwithflqgandlqgcontrolmethods |