Through this system, users can input parameters for a vehicle semi-active suspension using a magneto-rheological damper (sprung mass, sprung mass centroid position parameters, unsprung mass, magneto-rheological damper model parameters, suspension spring stiffness, wheel equivalent spring stiffness, balance bar torsion spring stiffness, and ground excitation). Through this program's calculations, a vehicle suspension deep reinforcement learning controller model can be obtained to optimize the shock absorption effect at the vehicle's center of mass. Development hardware environment: CPU Intel Core i5 11600k, Memory: 32GB, Hard disk space: 4TB; Runtime hardware environment: CPU Intel Core i5 10400, Memory: 8GB, Hard disk space: 500GB. Development software environment: Windows 10; Runtime software environment: Windows 10.
Yongjun Wang, Xiaoming Wang, Gang Zhi et al.· Zenodo (CERN European Organi...· 0 citations
Through this system, users can input parameters for a vehicle semi-active suspension using a magneto-rheological damper (sprung mass, sprung mass centroid position parameters, unsprung mass, magneto-rheological damper model parameters, suspension spring stiffness, wheel equivalent spring stiffness, balance bar torsion spring stiffness, and ground excitation). Through this program's calculations, a vehicle suspension deep reinforcement learning controller model can be obtained to optimize the shock absorption effect at the vehicle's center of mass. Development hardware environment: CPU Intel Core i5 11600k, Memory: 32GB, Hard disk space: 4TB; Runtime hardware environment: CPU Intel Core i5 10400, Memory: 8GB, Hard disk space: 500GB. Development software environment: Windows 10; Runtime software environment: Windows 10.
Yongjun Wang, Xiaoming Wang, Gang Zhi et al.· Zenodo (CERN European Organi...· 0 citations