Variable Universe Fuzzy Control for Direct Yaw Moment of Distributed Drive Electric Vehicle

  • Sen Cao
  • Yaping Wang
  • Haoran Jia
  • Zheng ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11743)


A direct yaw moment control (DYC) system based on variable universe fuzzy logic is proposed to improve the stability of distributed drive electric vehicle in this paper. The upper layer of controller is a two-stage variable universe fuzzy controller, and the deviation between actual value and reference of yaw rate and side slip angle is used to calculate the required yaw moment. The lower layer of the controller adopts the redistribution pseudo inverse (RPI) algorithm, which takes the tire utilization rate as the optimization target and the maximum driving torque of the motor as the constraint target, and effectively distributes the required yaw moment to each wheel. The proposed control system is simulated and verified under J-turn and single shift line condition, and the control effect is reflected by comparison control in the uncontrolled, fuzzy PI control and variable universe fuzzy control. The simulation results show that it can make the vehicle track the reference better and enhance vehicle’s handing and stability, and that the control algorithm is effective and feasible.


Distributed drive electric vehicle Direct yaw moment control Variable universe fuzzy control 


  1. 1.
    Habib, S., Khan, M.M., Abbas, F., et al.: A comprehensive study of implemented international standards, technical challenges, impacts and prospects for electric vehicles. IEEE Access 6, 13866–13890 (2018)CrossRefGoogle Scholar
  2. 2.
    Zhai, L., Sun, T., Wang, J.: Electronic stability control based on motor driving and braking torque distribution for a four in-wheel motor drive electric vehicle. IEEE Trans. Veh. Technol. 65, 4726–4739 (2016)CrossRefGoogle Scholar
  3. 3.
    Zhang, G., Zhang, H., Huang, X., et al.: Active fault-tolerant control for electric vehicles with independently driven rear in-wheel motors against certain actuator faults. IEEE Trans. Control Syst. Technol. 1–16 (2015)Google Scholar
  4. 4.
    Zhou, H., Chen, H., Ren, B., et al.: Yaw stability control for in-wheel-motored electric vehicle with a fuzzy PID method. In: 2015 27th Chinese Control and Decision Conference (CCDC). IEEE (2015)Google Scholar
  5. 5.
    Li, H., Zhihong, M., Jiayin, W.: Variable universe stable adaptive fuzzy control of nonlinear system. Sci. Chin. Ser. E Technol. Sci. 45(3), 225–240 (2002)MathSciNetCrossRefGoogle Scholar
  6. 6.
    He, P., Hori, Y.: Optimum traction force distribution for stability improvement of 4WD EV in critical driving condition. In: 2006 9th IEEE International Workshop on Advanced Motion Control. IEEE (2006)Google Scholar
  7. 7.
    Abe, M., Mokhiamar, O.: An integration of vehicle motion controls for full drive-by-wire vehicle. Proc. Inst. Mech. Eng. Part K: J. Multi-Body DynGoogle Scholar
  8. 8.
    Guvenc, B.A., Bunte, T., Odenthal, D., et al.: Robust two degree-of-freedom vehicle steering controller design. IEEE Trans. Control Syst. Technol. 12(4), 627–636 (2004)CrossRefGoogle Scholar
  9. 9.
    Sadri, S., Wu, C.Q.: Lateral stability analysis of on-road vehicles using the concept of Lyapunov exponents. In: IEEE Intelligent Vehicles Symposium (2012)Google Scholar
  10. 10.
    Zhang, B., Du, H., Lam, J., et al.: A novel observer design for simultaneous estimation of vehicle steering angle and sideslip angle. IEEE Trans. Ind. Electron. 63(7), 1 (2016)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Robotics and Intelligent Systems, School of Mechanical EngineeringXi’an Jiaotong UniversityXi’anChina
  2. 2.Institute of Automotive EngineeringShaanxi Communications Technical CollegeXianyangChina

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