A Dynamic Method to Forecast the Wheel Slip for Antilock Braking System and Its Experimental Evaluation

Abstract

The control of an antilock braking system (ABS) is a difficult problem due to its strongly nonlinear and uncertain characteristics. To overcome this difficulty, the integration of gray-system theory and sliding-mode control is proposed in this paper. This way, the prediction capabilities of the former and the robustness of the latter are combined to regulate optimal wheel slip depending on the vehicle forward velocity. The design approach described is novel, considering that a point, rather than a line, is used as the sliding control surface. The control algorithm is derived and subsequently tested on a quarter vehicle model. Encouraged by the simulation results indicating the ability to overcome the stated difficulties with fast convergence, experimental results are carried out on a laboratory setup. The results presented indicate the potential of the approach in handling difficult real-time control problems.

Firefly algorithm tuned fuzzy set-point weighted PID controller for antilock braking systems

“…A band-limited white noise is added at slip and velocity measurements of the system for getting more realistic results due to the fact that most of the practical systems are subjected to disturbances. The numerical value of noise power for slip and speed measurements are selected as 10 -5 and 0.2 respectively (Oniz, 2007;Oniz et al, 2009) In Figure 7, it is found that the mere Set-Point weighting with b=0.9, leads to a response with overshoot. This means that the vehicle experiences a front and back motion when applying the brake input, which is not a desirable one.…”

“…Therefore, intelligent controllers should be developed to deal with all these uncertainties. Many control strategies such as Sliding mode control (Harifi et al, 2005;Unsal, & Kachroo, 1999;Choi et al, 2002;Oniz, 2007;Oniz, et al, 2009), intelligent techniques using Fuzzy Logic (Mauer, 1995;Radac et al, 2008), Artificial Neural Networks (Layne et al, 1993;Lin & Hsu, 2003), and Neuro-fuzzy control (Topalov, et al, 2011) are reported earlier in literature. Genetic Algorithm is used in finding optimum values of fuzzy component (Yonggon & Stanislaw, 2002).…”

“…The controller is designed to regulate the wheel slip in optimum level (0.08 to 0.3). Computer simulations have been performed in MATLAB-SIMULINK, version 7.11.0 (R2010b) and the performance of the proposed controller has been compared with SPWPID, GAFSPWPID controllers and the controllers presented in Oniz, (2007) and Oniz, et al (2009) …”

Intelligent exponential sliding-mode control with uncertainty estimator for antilock braking systems

“…Though the PID control provides the favorable braking responses, the control performance is influenced in different road conditions. In [3,4], a sliding-mode control (SMC) is designed to maintain the slip with parameter deviations and disturbances; however, the control law results in chattering control inputs so as to cause damage to ABS. In [5,6], a nonlinear adaptive control is proposed to obtain favorable control performance; however, it requires the structure of the system uncertainties.…”

The Evaluation of Braking Performances of Mechanical Brake System on Oil Rig

“…There are also many evaluation methods for braking system. Oniz, Y [15] improved the control of an antilock braking system (ABS) and evaluated this control algorithm. Ma, Xiqin [16] proposed a new evaluation index to detect overall performance of rail transit brake system based on designing a system which can detect output pressure of rail transit brake system.…”