A new estimation approach of road friction coefficient and optimum wheel slip ratio for longitudinal and lateral vehicle dynamics control using active steering and braking

Authors

1 Assist. Prof., Department of Industrial and Mechanical Eng, Buein Zahra Technical University, Buein Zahra, Qazvin.

2 Assoc. Prof., Department of Mechanical Eng., K.N. Toosi University, Tehran, Iran.

Abstract

In this paper, an integrated control system of longitudinal, lateral and yaw vehicle dynamics is presented using active braking and active front steering (AFS) systems. The proposed active braking system based on sliding mode controller, includes two kinds of working modes of anti-locked brake and (ABS) and an electronic stability control (ESC) and a fuzzy controller has been used in the AFS system. Also, a nonlinear estimator utilising unscented Kalman filter is applied to estimate the vehicle dynamics variables. According to the estimated values and Dugoff tire model, the tire-road friction coefficient is calculated. Since the ABS performance for shortening the stopping distance is dependent on the optimum wheel slip ratio, an adaptive neuro-fuzzy inference system (ANFIS) is proposed to obtain the optimum value. The tire-road friction coefficient, longitudinal velocity and the vertical load of each wheel are considered as the ANFIS inputs. In the simulation part, first, the hard braking action in straight line on the roads with various friction coefficients during driving is investigated, which results in high precision of the estimator for the friction coefficient and optimum wheel slip ratio, and greatly reduced the distance and stopping time in comparison to the vehicle without estimator. Then, simulation of split-μ roads has been carried out which demonstrates the integrated control of ABS, ESC and AFS systems associated with the mentioned estimators can, in addition to improving the lateral and yaw stability, also decrease the stopping distance.

Keywords

Main Subjects


[1] Karbalaei R, Ghaffari A, Kazemi R, Tabatabaei H (2008) A new intelligent strategy to integrated control of AFS/DYC based on fuzzy logic. Int J Math Phys Eng Sci 1(1): 47-52.
[2] Hwang T, Park KA, Heo S, Lee S, Lee J (2008) Design of integrated chassis control logics for AFS and ESP. Int J Automot Techn 9(1): 17-27.
[3] Naraghi M, Roshanbin A, Tavasoli A (2010) Vehicle stability enhancement - an adaptive optimal approach to the distribution of tyre forces. P I Mech Eng D-J Aut 224(4): 443-453.
[4] Ding N, Taheri S (2010) An adaptive integrated algorithm for active front steering and direct yaw moment control based on direct Lyapunov method. Vehicle Syst Dyn 48(10): 1193-1213.
[5] Doumiati M, Sename O, Dugard L, Gaspar P, Szabo Z (2013) Integrated vehicle dynamics control via coordination of active front steering and rear braking. Eur J Control 19(2): 121-143.
[6] Jalali M, Khosravani S, Khajepour A, Chen S, Litkouhi B (2017) Model predective control of vehicle stability using coordinated active steering and differential brakes. Mechatronics 48: 30-41.
[7] Zhang J, Li J (2019) Integrated vehicle chassis control for active front steering and direct yaw moment control based on hierarchical structure. T I Meas Control41(9): 2428-2440.
 [8] Ahn C, Kim B, Lee M (2012) Modeling and control of an anti-lock brake and steering system for cooperative control on split-μ surfaces. Int J Automot Techn 13(4): 571-581.
[9] Mirzaeinejad H, Mirzaei M, Kazemi R (2016) Enhancement of vehicle braking performance on split-μ roads using optimal integrated control of steering and braking systems. P I Mech Eng K-J Mul 230(4): 401-415.
[10] Song J (2012) Integrated control of brake pressure and rear-wheel steering to improve lateral stability with fuzzy logic. Int J Automot Techn 13(4): 563-570.

[11] Aalizadeh B (2019) A neurofuzzy controller for active front steering system of vehicle under road friction uncertainties. T I Meas Control 41(4): 1057-1067.

[12] Zhang X, Xu Y, Pan M, Ren F (2014) A vehicle ABS adaptive sliding-mode control algorithm based on the vehicle velocity estimation and tyre/road friction coefficient estimations. Vehicle Syst Dyn 52(4): 475-503.

[13] Bagheri A, Azadi S, Soltani A (2017) A combined use of adaptive sliding mode control and unscented Kalman filter estimator to improve vehicle yaw stability. P I Mech Eng K-J Mul 231(2): 388-401.
[14] Paul D, Velenis E, Humbert F, at el (2019) Tyre–road friction μ-estimation based on braking force distribution. P I Mech Eng D-J Aut 233(8): 2030-2047.
[15] Novi T, Capitani R, Annicchiarico C (2019) An integrated artificial neural network–unscented Kalman filter vehicle sideslip angle estimation based on inertial measurement unit measurements. P I Mech Eng D-J Aut 233(7): 1864-1878.
[16] Ahmadi Jeyed H, Ghaffari A (2019) Nonlinear estimator design based on extended Kalman filter approach for state estimation of articulated heavy vehicle. P I Mech Eng K-J Mul 233(2): 254-265.
[17]  باقری ا، آزادی ش، سلطانی ع (1396) بهبود پایداری چرخشی خودرو توسط سیستم ترمز فعال با استفاده از کنترل مود لغزشی. مجله علمی پژوهشی مکانیک سازه­ها و شاره­ها 78-65 :(1)7. 7
[18] Ren H, Chen S, Shim T, Wu, Z (2014) Effective assessment of tyre-road friction coefficient using a hybrid estimator. Vehicle Syst Dyn 52(8): 1047-1065.
[19] مشهدی ب، مجیدی م (1387) طراحی کنترلر فازی یکپارچه سیستم­های فرمان فعال و کنترل پایداری خودرو. دومین کنگره مشترک سیستم­های فازی و هوشمند ایران، تهران، دانشگاه صنعتی مالک اشتر.