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Model Predictive Control of a Semi-Active Vehicle-Mounted Vibration Isolation Platform.
Wu, Liang; Zhang, Weizhou; Yuan, Daofa; Youn, Iljoong; Jia, Weiwei.
Afiliación
  • Wu L; State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130012, China.
  • Zhang W; Changsha Automotive Innovation Research Institute, Changsha 410036, China.
  • Yuan D; State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130012, China.
  • Youn I; State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130012, China.
  • Jia W; School of Mechanical and Aerospace Engineering, Gyeongsang National University, Jinju 660-701, Republic of Korea.
Sensors (Basel) ; 24(1)2023 Dec 31.
Article en En | MEDLINE | ID: mdl-38203106
ABSTRACT
When conventional delivery vehicles are driven over complex terrain, large vibrations can seriously affect vehicle-loaded equipment and cargo. Semi-active vehicle-mounted vibration isolation control based on road preview can improve the stability of loaded cargo and instruments by enabling them to have lower vertical acceleration. A combined dynamic model including a vehicle and platform is developed first. In order to obtain a non-linear relationship between damping force and input current, a continuous damping control damper model is developed, and the corresponding external characteristic tests are carried out. Because some conventional control algorithms cannot handle complex constraints and preview information, a model predictive control algorithm based on forward road preview and input constraints is designed. Finally, simulations and real tests of the whole vehicle vibration environment are carried out. The results show that the proposed model predictive control based on road preview can effectively improve vibration isolation performance of the vehicle-mounted platform.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: China