Robust adaptive anti-slip regulation controller for a distributed-drive electric vehicle considering the driver’s intended driving torque

Abstract

A robust adaptive anti-slip regulation controller which consists of two components, namely a road friction coefficient estimator and a wheel dynamics controller, is designed for distributed-drive electric vehicles. The road friction coefficient estimator is based on the latest non-affine parameter estimation theory to achieve the peak road friction coefficient. Also, working conditions for the road friction coefficient estimator are proposed to avoid the estimation error caused by a small slip ratio. According to the results of the road friction coefficient estimator, the desired reference slip ratio is obtained and the key parameters of the robust adaptive anti-slip regulation controller are modified to make sure that the road conditions can be made full use of. Then, according to the desired reference slip ratio, a state feedback control law with a conditional integrator is designed on the basis of the Lyapunov stability theory for a wheel dynamics controller by analysis of the non-linear characteristics of the tyres and the driver’s intended driving torque and constraints from the ground–tyre adhesion. In addition, it achieves smooth switching between optimal driving and the driver’s intended driving torque rather than normal switching logic. Multi-condition simulations and experiments show that the controller is adaptive to different road conditions, can improve the driving efficiency of the vehicle and can ensure stability of the vehicle. Finally, with comparative experiments, the distributed-drive electric vehicle with a robust adaptive anti-slip regulation controller proves to be more efficient than the traditional vehicle with a traditional anti-slip regulation controller.

Keywords

Anti-slip regulation robust adaptive driver’s intended driving torque smooth switching

Get full access to this article

View all access options for this article.

References

Pasillaslépine

. Hybrid modeling and limit cycle analysis for a class of five-phase anti-lock brake algorithms. Veh System Dynamics 2006; 44(2):173–188.

Tanelli

Osorio

Bernardo

et al . Limit cycles analysis in hybrid anti-lock braking systems. In: 2007 46th IEEE conference on decision and control, New Orleans, Louisiana, USA, 12–14 December 2007, pp. 3865–3870. New York: IEEE.

Imsland

Johansen

Fossen

et al . Vehicle velocity estimation using nonlinear observers. Automatica 2006; 42(12): 2091–2103.

Grip

Imsland

Johansen

Fossen

. Nonlinear vehicle velocity observer with road–tire friction adaptation. In: 2007 46th IEEE conference on decision and control, New Orleans, Louisiana, USA, 12–14 December 2007, pp. 3603–3608. New York: IEEE.

Amodeo

Ferrara

Terzaghi

Vecchio

. Wheel slip control via second-order sliding-mode generation. IEEE Trans Intell Transpn Systems 2010; 11(1): 122–131.

Kayacan

Oniz

Kaynak

. A grey system modeling approach for sliding-mode control of antilock braking system. IEEE Trans Ind Electron 2009; 56(8): 3244–3252.

Schinkel

Hunt

. Anti-lock braking control using a sliding mode like approach. In: 2002 American control conference, Anchorage, Alaska, USA, 8–10 May, 2002, pp. 2386–2391. New York: IEEE.

Buckholtz

. Reference input wheel slip tracking using sliding mode control. SAE paper 2002-01-0301, 2002.

Mutoh

Hayano

Yahagi

Takita

. Electric braking control methods for electric vehicles with independently driven front and rear wheels. IEEE Trans Ind Electron 2007; 54(2): 1168–1176.

10.

Johansen

Petersen

Kalkkuhl

Lüdemann

. Gain-scheduled wheel slip control in automotive brake systems. IEEE Trans Control Systems Technol 2003; 11(6): 799–811.

11.

Gustafsson

. Estimation and change detection of tire–road friction using the wheel slip. In: 1996 IEEE international symposium on computer-aided control system design, Dearborn, Michigan, USA, 15–18 September 1996, pp. 99–104. New York: IEEE.

12.

Gustafsson

. Monitoring tire–road friction using the wheel slip. IEEE Control Systems 2010; 18(4): 42–49.

13.

Gustafsson

. Slip-based tire-road friction estimation. Automatica 1995; 33(6): 1087–1099.

14.

Germann

Wurtenberger

Daiss

. Monitoring of the friction coefficient between tyre and road surface. In: 3rd IEEE conference on control applications, Glasgow, UK, 24–26 August 1994, Vol 1, pp. 613–618. New York: IEEE.

15.

Muller

Uchanski

Hedrick

. Estimation of the maximum tire–road friction coefficient. Trans ASME, J Dynamic Systems, Measmt, Control 2003; 125(4): 607–617.

16.

Alvarez

Claeys

Horowitz

. Emergency braking control with an observer-based dynamic tire/road friction model and wheel angular velocity measurement. Veh System Dynamics 2003; 39(2): 81–97.

17.

Tjonnas

Johansen

. Stabilization of automotive vehicles using active steering and adaptive brake control allocation. IEEE Trans Control Systems Technol 2010; 18(3): 545–558.

18.

Grip

. Topics in state and parameter estimation for nonlinear and uncertain systems. PhD Thesis, Norwegian University of Science and Technology, Trondheim, Norway, 2010.

19.

Kay

Khalil

. Universal controllers with nonlinear integrators. In: 2002 American control conference, Anchorage, Alaska, USA, 8–10 May 2002, pp. 116–121. New York : IEEE.

20.

Gao

Feng

Xiong

. Vehicle longitudinal velocity estimation with adaptive Kalman filter. In: FISITA 2012 world automotive congress, Beijing, People’s Republic of China, 27–30 November 2012, Lecture Notes in Electrical Engineering, Vol 198, pp. 415–423. Berlin: Springer.

21.

Jin

Xiong

Feng

. Path following control for skid steering vehicles with vehicle speed adaption. SAE paper 2014-01-0277, 2014.