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Research Papers

Comparison of Adaptive Fuzzy Sliding-Mode Pulse Width Modulation Control With Common Model-Based Nonlinear Controllers for Slip Control in Antilock Braking Systems

[+] Author and Article Information
Alireza Mousavi

Mechanical Engineering Department,
Iran University of Science and Technology,
Narmak,
Tehran 16844, Iran
e-mail: alirezamousavi@alumni.iust.ac.ir

Amir H. Davaie-Markazi

Mechanical Engineering Department,
Iran University of Science and Technology,
Narmak,
Tehran 16844, Iran
e-mail: markazi@iust.ac.ir

Saleh Masoudi

Mechanical Engineering Department,
Iran University of Science and Technology,
Narmak,
Tehran 16844, Iran
e-mail: salehmasoudi@alumni.iust.ac.ir

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received December 10, 2016; final manuscript received July 3, 2017; published online September 8, 2017. Assoc. Editor: Shankar Coimbatore Subramanian.

J. Dyn. Sys., Meas., Control 140(1), 011014 (Sep 08, 2017) (15 pages) Paper No: DS-16-1593; doi: 10.1115/1.4037296 History: Received December 10, 2016; Revised July 03, 2017

A novel application of the adaptive fuzzy sliding-mode control (AFSMC) to the case of an antilock braking system (ABS) is proposed in this paper. ABS is a system in vehicles that allows the wheels to maintain tractive contact with the road and avoid uncontrolled skidding. By using ABS, the stopping distances on dry and slippery surfaces are expected to decrease. The maximum braking force is a nonlinear function of the slip ratios of the wheels, which is sensitive to the vehicle weight and road condition. In this research, a simple low-order model of the braking dynamics is considered and unmodeled dynamics are taken as uncertainties. The robust AFSMC method is used to regulate the wheel slip ratio toward the desired value. The proposed controller employs pulse width modulation (PWM) to generate the braking torque. There is no need to use any reference measured data or experimental knowledge of relevant experts to design the controller. A clear advantage is that the designed controller does not rely on the nonlinear tire–road friction model. The second Lyapunov theorem is employed to prove the closed-loop asymptotic stability. In the simulations, the multibody dynamics method is used for modeling the longitudinal motion of SAIPA X100 and X200 vehicle platforms. Furthermore, the actuation and the switching dynamics of the braking system are taken into account. Resulting performance is compared to the conventional sliding-mode and feedback linearization methods. Analysis of the simulation results reveals the effectiveness of proposed AFSMC method.

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Figures

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Fig. 1

The free body diagram of the quarter vehicle model

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Fig. 2

The friction coefficient curves for different road conditions

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Fig. 3

Gaussian membership functions of s for AFSMC for the anti-lock braking system

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Fig. 4

The structure of the adaptive fuzzy sliding-mode controller

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Fig. 5

Photographs of (a) Pride X100 and (b) Tiba X200 vehicles

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Fig. 6

Pride X100 simulated results using MGH-60 controller on the dry asphalt road: (a) slip ratio, (b) vehicle and wheel velocities, (c) braking actuator input, and (d) braking torque (v0 = 100 km/h)

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Fig. 7

Pride X100 simulated results using feedback linearization control method on the dry asphalt road: (a) slip ratio, (b) vehicle and wheel velocities, (c) braking actuator input, and (d) braking torque (v0 = 100 km/h)

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Fig. 8

Pride X100 simulated results using SMC method on the dry asphalt road: (a) slip ratio, (b) vehicle and wheel velocities, (c) braking actuator input, and (d) braking torque (v0 = 100 km/h)

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Fig. 9

Pride X100 simulated results using AFSMC method on the dry asphalt road: (a) slip ratio, (b) vehicle and wheel velocities, (c) braking actuator input, and (d) braking torque (v0 = 100 km/h)

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Fig. 10

Comparison of the stopping distances from initial velocities of (a) v0 = 50 km/h and (b) v0 = 100 km/h on the dry asphalt road

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Fig. 11

Pride X100 simulated results using MGH-60 control method on the wet asphalt road: (a) slip ratio and (b) vehicle and wheel velocities (v0 = 100 km/h)

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Fig. 12

Pride X100 simulated results using feedback linearization control method on the wet asphalt road: (a) slip ratio and (b) vehicle and wheel velocities (v0 = 100 km/h)

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Fig. 13

Pride X100 simulated results using SMC method on the wet asphalt road: (a) slip ratio and (b) vehicle and wheel velocities (v0 = 100 km/h)

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Fig. 14

Pride X100 simulated results using AFSMC method on the wet asphalt road: (a) slip ratio and (b) vehicle and wheel velocities (v0 = 100 km/h)

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Fig. 15

Tiba X200 vehicle and wheel velocities on the wet asphalt road using (a) feedback linearization, (b) SMC, (c) AFSMC, and (d) MGH-60 methods (v0 = 100 km/h)

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Fig. 16

Pride X100 vehicle and wheel velocities during jump condition using (a) feedback linearization, (b) SMC, (c) AFSMC, and (d) MGH-60 methods (v0 = 50 km/h)

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Fig. 17

Pride X100 vehicle and wheel velocities on the snow using (a) feedback linearization, (b) SMC, (c) AFSMC, and (d) MGH-60 methods (v0 = 30 km/h)

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Fig. 18

Pride X100 vehicle and wheel velocities on the ice using (a) feedback linearization, (b) SMC, (c) AFSMC, and (d) MGH-60 methods (v0 = 30 km/h)

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