Semiactive Self-Tuning Fuzzy Logic Control of Full Vehicle Model with MR Damper | Zendy

Paksoy Mahmut | Zendy; Guclu Rahmi | Zendy; Cetin Saban | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Semiactive Self-Tuning Fuzzy Logic Control of Full Vehicle Model with MR Damper

Author(s) -

Paksoy Mahmut,

Guclu Rahmi,

Cetin Saban

Publication year - 2014

Publication title -

advances in mechanical engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.318

H-Index - 40

ISSN - 1687-8132

DOI - 10.1155/2014/816813

Subject(s) - control theory (sociology) , damper , magnetorheological damper , fuzzy logic , magnetorheological fluid , engineering , suspension (topology) , vibration , control engineering , reduction (mathematics) , fuzzy control system , matlab , controller (irrigation) , computer science , mathematics , control (management) , artificial intelligence , agronomy , physics , geometry , quantum mechanics , homotopy , pure mathematics , biology , operating system

Intelligent controllers are studied for vibration reduction of a vehicle consisting in a semiactive suspension system with a magnetorheological(MR) damper. The vehicle is modeled with seven degrees of freedom as a full vehicle model. The semiactive suspension system consists of a linear spring and an MR damper. MR damper is modeled using Bouc-Wen hysteresis phenomenon and applied to a full vehicle model. Fuzzy Logic based controllers are designed to determine the MR damper voltage. Fuzzy Logic and Self-Tuning Fuzzy Logic controllers are applied to the semiactive suspension system. Results of the system are investigated by simulation studies in MATLAB-Simulink environment. The performance of the semiactive suspension system is analyzed with and without control. Simulation results showed that both Fuzzy Logic and Self-Tuning Fuzzy Logic controllers perform better compared to uncontrolled case. Furthermore, Self-Tuning Fuzzy Logic controller displayed a greater improvement in vibration reduction performance compared to Fuzzy Logic controller.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research