Abstract
This paper proposed a torque distribution strategy based on linear time-varying radial basis function (LTVRBF) neural networks for yaw stability control of electric vehicles equipped with in-wheel motors. The desired yaw rate is calculated by the two-degree-of-freedom (2- DOF) bicycle dynamic model. The influence of the timevarying steering angle is considered for reducing the yaw rate error. To solve this problem, the constant connection weight of conventional RBF networks is converted into a time-varying variable which is used to track the reference trajectory and optimize the torque distribution. The torques of in-wheel motors are restricted in high efficiency range to improve the system energy efficiency. Simulation results of the proposed torque distribution strategy based on dSPACE simulator show that LTV-RBF networks can effectively track the reference yaw rate and stabilize the system.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: DEStech Transactions on Environment, Energy and Earth Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
