Longitudinal speed control of autonomous vehicle based on a self-adaptive PID of radial basis function neural network

Longitudinal speed control of autonomous vehicle based on a self-adaptive PID of radial basis function neural network

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The tracking accuracy of speed plays a significant role in the autonomous vehicle's control and safety management. In this study, we presented a novel method called self-adaptive proportional integral derivative (PID) of radial basis function neural network (RBFNN-PID) which is shown with improved longitudinal speed tracking accuracy for autonomous vehicles. A forward simulation model of longitudinal speed control for autonomous vehicles is established based on the driver model of self-adaptive RBFNN-PID and the vehicle dynamics model. Based on that, we used the traditional PID and fuzzy control methods as benchmarks to demonstrate the edge of the self-adaptive RBFNN-PID control under the new European driving cycle. Simulation results show the RBFNN-PID method is significantly more precise than the comparing groups, with a reduced error in the range of [−0.369, 0.203] m/s. The vehicle performance gives better ride comfort as well. In all, self-adaptive RBFNN-PID is proven to be effective in longitudinal speed control of autonomous vehicles and significantly outperforms the other two methods.


    1. 1)
      • 1. Lin, Y., Tang, P., Zhang, W.J., et al: ‘Artificial neural network modelling of driver handling behaviour in a driver-vehicle-environment system’, Int. J. Veh. Des., 2005, 37, (1), pp. 2445.
    2. 2)
      • 2. Guo, K., Guan, H.: ‘Modelling of driver/vehicle directional control system’, Veh. Syst. Dyn., 2007, 22, (3-4), pp. 141184.
    3. 3)
      • 3. Luo, L.H., Liu, H., Li, P., et al: ‘Model predictive control for adaptive cruise control with multi-objectives: comfort, fuel-economy, safety and car-following’, J. Zhejiang Univ. Sci. A (Appl. Phys. Eng.), 2010, 11, (3), pp. 191201.
    4. 4)
      • 4. Wu, L.J., Liu, Z.D., Ma, Y.F.: ‘Longitudinal control strategy for vehicle adaptive cruise control systems’, J. Beijing Inst. Technol., 2007, 16, (1), pp. 2832.
    5. 5)
      • 5. Rutland, N.K.: ‘Illustration of a new principle of design: vehicle speed control’, Int. J. Control, 1992, 55, (6), pp. 13191334.
    6. 6)
      • 6. Gerdes, J.C., Hedriek, J.K.: ‘Vehicle speed and spacing via coordinated throttle and brake actuation’, Control Eng. Pract., 1997, 5, (11), pp. 16071614.
    7. 7)
      • 7. Kuragaki, S., Minowa, T., Kayano, M., et al: ‘An adaptive cruise control using wheel torque management technique’, SAE Trans., 1998, 107, pp. 11221126.
    8. 8)
      • 8. Yi, K., Hong, J., Kwon, Y.D.: ‘A vehicle control algorithm for stop and go cruise control’, Proc. Inst. Mech. Eng., Part D: J. Automob. Eng., 2001, 215, (10), pp. 10991115.
    9. 9)
      • 9. Liang, H., Chong, K.T., No, T.S., et al: ‘Vehicle longitudinal brake control using variable parameter sliding control’, Control Eng. Pract., 2003, 11, (4), pp. 403411.
    10. 10)
      • 10. Bin, Y., Li, K.Q., Feng, N.L.: ‘Disturbance decoupling robust control of vehicle full speed cruise dynamic system’, Sci. China Series E: Technol. Sci., 2009, 12, (52), pp. 35453564.
    11. 11)
      • 11. Majdoub, K.E., Ouadi, H.: ‘Vehicle longitudinal control using Kiencke's tire model and sliding mode control design’, IFAC Proc. Vol., 2010, 14, (43), pp. 903908.
    12. 12)
      • 12. Zhu, M., Chen, H., Xiong, G.: ‘A model predictive speed tracking control approach for autonomous ground vehicles’, Mech. Syst. Signal Process., 2016, 87, (3), pp. 138152.
    13. 13)
      • 13. Zalila, Z., Lezy, P.: ‘Longitudinal control of an autonomous vehicle through a hybrid fuzzy/classical controller’. Conf. Record on WES-CON/94 Idea/Microelectronics., 1994, pp. 118124.
    14. 14)
      • 14. Fritz, H.: ‘Neural speed control for autonomous road vehicles’, Control Eng. Pract., 1996, 4, (4), pp. 507512.
    15. 15)
      • 15. Shukla, S., Tiwari, M.: ‘Fuzzy logic of speed and steering control system for three dimensionals line following of an autonomous vehicle’, Int. J. Comput. Sci. Inf. Secur., 2010, 7, (3), pp. 101108.
    16. 16)
      • 16. Khooban, M.H., Vafamand, N., Niknam, T.: ‘T–S fuzzy model predictive speed control of electrical vehicles’, ISA Trans., 2016, 4, (19), pp. 231240.
    17. 17)
      • 17. Kumar, V., Rana, K.P.S., Mishra, P.: ‘Robust speed control of hybrid electric vehicle using fractional order fuzzy PD and PI controllers in cascade control loop’. J. Franklin Inst., 2016, 8, (353), pp. 17131741.
    18. 18)
      • 18. Zhang, Y., Chen, X., Zhang, X., et al: ‘Dynamic modeling and simulation of a dual-clutch autonomous lay-shaft transmission’, ASME J. Mech. Des., 2005, 127, (2), pp. 302307.
    19. 19)
      • 19. Wang, S., Xu, X.Y., Liu, Y.F., et al: ‘Design and dynamic simulation of hydraulic system for a new automatic transmission’, J. Cent. South Univ. Technol., 2009, 16, (4), pp. 670701.
    20. 20)
      • 20. Sato, K., Ito, Y.: ‘Development of 4 speed automatic transmission with line pressure control system’, Suzuki Tech. Rev., 1999, 25, (14), pp. 2126.
    21. 21)
      • 21. Guo, K.H., Ding, H., Zhang, J., et al: ‘Development of a longitudinal and lateral driver model for autonomous vehicle control’, Int. J. Veh. Des., 2004, 36, (1), pp. 5065.
    22. 22)
      • 22. Yamamoto, T., Omatu, S., Kaneda, M.: ‘A design method of self-tuning PID controllers’. American Control Conf., 1994, pp. 32533267.
    23. 23)
      • 23. Husain, H., Khalid, M., Yusof, R.: ‘Nonlinear function approximation using radial basis function neural networks’. 2002 Student Conf. Research and Development, 2002, vol. 522, pp. 326329.
    24. 24)
      • 24. Schweitzer, J., Gandham, J.: ‘Computational fluid dynamics in torque converters: validation and application’, Int. J. Rotat. Mach., 2003, 9, (6), pp. 411418.
    25. 25)
      • 25. Anderson, C.L., Zeng, L., Sweger, P.O., et al: ‘Experimental investigation of cavitation signatures in an automotive torque converter using a microwave telemetry technique’, Int. J. Rotat. Mach., 2003, 9, (6), pp. 403410.
    26. 26)
      • 26. Hahn, J.O., Lee, K.I.: ‘Nonlinear robust control of torque converter clutch slip system for passenger vehicles using advanced torques estimation algorithms’, Veh. Syst. Dyn., 2002, 3, (3), pp. 175192.
    27. 27)
      • 27. Wipke, K.B., Cuddy, M.R., Burch, S.D.: ‘ADVISOR 2.1: a user-friendly advanced powertrain simulation using a combined backward /forward approach’, IEEE Trans. Veh. Technol., 1999, 48, (6), pp. 17511761.
    28. 28)
      • 28. Wilcutts, M., Souder, J.: ‘Powertrain model development for mobiles(Vehicle Dynamics Laboratory, University of California, Berkeley, 2002).

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