Uncertainty-Estimation-Based Approach to Antilock Braking Systems

Patil, Adarsh; Ginoya, Divyesh; Shendge, P. D.; Phadke, S. B.

doi:10.1109/tvt.2015.2413451

Cited by 50 publications

(16 citation statements)

References 41 publications

(34 reference statements)

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“…As seen from Equation (10), the relationship between the braking pressure p and the rotation angle of motor θ m is established by a linear function. However, the ideal EMA mathematical model ignored many nonlinear factors, such as the deformation of the gear and gear clearance.…”

Section: Deformation Of the Reduction Gearmentioning

confidence: 99%

“…With the development of permanent magnet material technology, the brushless DC motor (BLDCM) has been widely used in many industrial and automotive applications. The BLDCM has the advantages of a high-power density, a compact structure, and high efficiency which make it suitable for driving the EMA [10][11][12]. From the analysis above, it can be noted that the electric braking system is a new kind of servo system with special applications.…”

Section: Introductionmentioning

confidence: 99%

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Backstepping Adaptive Neural Network Control for Electric Braking Systems of Aircrafts

Zhang

Hui

2019

Algorithms

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This paper proposes an adaptive backstepping control algorithm for electric braking systems with electromechanical actuators (EMAs). First, the ideal mathematical model of the EMA is established, and the nonlinear factors are analyzed, such as the deformation of the reduction gear. Subsequently, the actual mathematical model of the EMA is rebuilt by combining the ideal model and the nonlinear factors. To realize high performance braking pressure control, the backstepping control method is adopted to address the mismatched uncertainties in the electric braking system, and a radial basis function (RBF) neural network is established to estimate the nonlinear functions in the control system. The experimental results indicate that the proposed braking pressure control strategy can improve the servo performance of the electric braking system. In addition, the hardware-in-loop (HIL) experimental results show that the proposed EMA controller can satisfy the requirements of the aircraft antilock braking systems.

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Section: Deformation Of the Reduction Gearmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Backstepping Adaptive Neural Network Control for Electric Braking Systems of Aircrafts

Zhang

Hui

2019

Algorithms

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“…De Castro et al [30] proposed a sliding mode control (SMC) approach based on field programmable gate array technology, while Okyay et al [1] proposed an SMC structure employing derivative switching function with integral sliding surface. The SMC is widely used in braking systems to overcome the system uncertainties [31], which has high robustness to the external disturbance [32], sensor noise [33] and modeling error [34]. However, it is generally cumbersome in controller design for the braking system.…”

Section: Introductionmentioning

confidence: 99%

Two-Time-Scale Braking Controller Design With Sliding Mode for Electric Vehicles Over CAN

Zhu

et al. 2019

IEEE Access

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This paper proposed a braking torque controller via two-time-scale design with a sliding mode for electric vehicles with four in-wheel motors. According to the different changing rates between the vehicle and wheel motion during the braking process, the design of the braking controller is carried out in two steps. In the first step, a nominal braking controller is developed over the slow-time scale without considering the tire-road friction. Then, a tire-road friction observer is adopted in the fast-time scale to recover the performance of the nominal braking controller. Owning to the high nonlinearity and complexity of the braking system, a sliding mode surface is further added in the nominal braking controller to ensure the stability and robustness of the proposed braking controller. A braking supervisor is adopted to enable the proposed braking controller, which is based on the wheel slip as well as vehicle speed condition. And a torque allocation scheme is presented for the coordination between the regenerative braking system and the friction braking system in each wheel. Co-simulation is conducted using MATLAB/Simulink and CarSim. The effectiveness of proposed controller under different braking conditions is fully validated. A delicate controller area network (CAN) bus model is developed via SimEvent, by which the robust performance of proposed braking controller against CAN-induced time-varying delays is also investigated.INDEX TERMS Braking control, two-time-scale design, sliding mode, CAN-induced delays, electric vehicle.

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“…Adaptive SMC for control of ABS torque to secure optimal slip ratio following is presented in [1]. Employing backstepping control for ABS is the subject of study in [18]. Quasi-sliding mode Control along with the orthogonal endocrine Neural Network estimator for ABS control has been detailed in [19].…”

Section: Introductionmentioning

confidence: 99%

Adaptive optimal slip ratio estimator for effective braking on a non-uniform condition road

Seyedtabaii

Velayati

2019

Automatika

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In this paper, an adaptive algorithm is developed which senses the road condition change and estimates a (time-varying) optimal braking slip ratio. This is conducted by two on-line simultaneously operating tire-road friction-curve slope calculators: one based on the accelerometer output and the other based on the wheel speed. The required vehicle speed is estimated using a robust sliding-mode observer. Enforcement of the online optimal braking reference is left to an adaptive sliding mode controller to cope with the system strong nonlinearity, time dependency and the speed and friction-coefficient estimation errors. The algorithm is applied to a half model car and the braking performance is examined. The results indicate that the proposed algorithm substantially reduces the stopping time and distance. The performance of the algorithm is verified using different vehicle initial speeds and especially non-uniform road condition where 8% improvement versus the nonadaptive optimal slip ratio algorithm is recorded. ARTICLE HISTORY

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Uncertainty-Estimation-Based Approach to Antilock Braking Systems

Cited by 50 publications

References 41 publications

Backstepping Adaptive Neural Network Control for Electric Braking Systems of Aircrafts

Backstepping Adaptive Neural Network Control for Electric Braking Systems of Aircrafts

Two-Time-Scale Braking Controller Design With Sliding Mode for Electric Vehicles Over CAN

Adaptive optimal slip ratio estimator for effective braking on a non-uniform condition road

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