Adaptive Fuzzy Fractional-Order Sliding Mode Controller Design for Antilock Braking Systems

Author(s):  
Yinggan Tang ◽  
Ying Wang ◽  
Mingyu Han ◽  
Qiusheng Lian

Antilock braking system (ABS) has been designed to attain maximum negative acceleration and prevent the wheels from locking. Many efforts had been paid to design controller for ABS to improve the brake performance, especially when road condition changes. In this paper, an adaptive fuzzy fractional-order sliding mode controller (AFFOSMC) design method is proposed for ABS. The proposed AFFOSMC combines the fractional-order sliding mode controller (FOSMC) and fuzzy logic controller (FLC). In FOSMC, the sliding surface is PDα, which is based on fractional calculus (FC) and is more robust than conventional sliding mode controllers. The FLC is designed to compensate the effects of parameters varying of ABS. The tuning law of the controller is derived based on Lyapunov theory, and the stability of the system can be guaranteed. Simulation results demonstrate the effectiveness of AFFOSMC for ABS under different road conditions.

Author(s):  
Yesim Oniz ◽  
Erdal Kayacan ◽  
Okyay Kaynak

The main control objective of an Antilock Braking System (ABS) is to increase the tractive forces between wheel and road surface by keeping the wheel slip at the peak value of μ – λ curve. Conventionally, it is assumed that optimal wheel slip is constant. In this paper, a grey sliding mode controller is proposed to regulate optimal wheel slip depending on the vehicle forward velocity. ABS exhibits strongly nonlinear and uncertain characteristics. To overcome these difficulties, robust control methods should be employed. The concept of grey system theory, which has a certain prediction capability, offers an alternative approach to conventional control methods. The proposed controller anticipates the upcoming values of wheel slip and optimal wheel slip, and takes the necessary action to keep wheel slip at the desired value. The control algorithm is applied to a quarter vehicle model, and it is verified through simulations indicating fast convergence and good performance of the designed controller.


2013 ◽  
Vol 111 ◽  
pp. 122-130 ◽  
Author(s):  
Yinggan Tang ◽  
Xiangyang Zhang ◽  
Dongli Zhang ◽  
Gang Zhao ◽  
Xinping Guan

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