Interval Type-2 Fuzzy Logic Anti-Lock Braking Control for Electric Vehicles under Complex Road Conditions

Lv, Lin-Feng; Wang, Juncheng; Jiang, Long

doi:10.3390/su132011531

Cited by 12 publications

(5 citation statements)

References 32 publications

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“…Assuming that the stator windings are uniformly distributed and air gap magnetic field is trapezoidal distribution, the phase voltage equation can be calculated as following [25,26]:…”

Section: Hub-wheel Motor Modelmentioning

confidence: 99%

“…Assuming that the stator windings are uniformly distributed and air gap magnetic field is trapezoidal distribution, the phase voltage equation can be calculated as following [25, 26]:

([], \begin{array}{l} u_{a} \\ u_{b} \\ u_{c} \end{array}) goodbreak= ([], \begin{array}{c} R & 0 & 0 \\ 0 & R & 0 \\ 0 & 0 & R \end{array}) ([], \begin{array}{l} i_{a} \\ i_{b} \\ i_{c} \end{array}) goodbreak+ ([], \begin{array}{c} L_{s} & 0 & 0 \\ 0 & L_{s} & 0 \\ 0 & 0 & L_{s} \end{array}) \frac{d}{dt} ([], \begin{array}{l} i_{a} \\ i_{b} \\ i_{c} \end{array}) goodbreak+ ([], \begin{array}{l} e_{a} \\ e_{b} \\ e_{c} \end{array})

where

u_{a, b, c}

are expressed as stator phase voltage, respectively, V ;

i_{a, b, c}

are indicated as stator phase current, respectively, A ;

e_{a, b, c}

are shown as counter electromotive force, respectively, V ; R is the stator phase resistance, Ω;

L_{s}

is the equivalent inductance, H .…”

Section: Mathematical Model Of Research Objectmentioning

confidence: 99%

See 1 more Smart Citation

A μ‐H_∞ control strategy for decreasing torque fluctuation of electro‐hydraulic integrated braking system in mode switching

Zhang

Zhao

Wang

et al. 2023

Asian Journal of Control

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Due to the difference of time‐domain response between hydraulic braking and regenerative braking, as well as changes of equivalent parameters and operating parameters during braking mode switching, it is liable to cause torque fluctuation, which affects braking safety and vehicle ride comfort. First, the uncertainties of vehicle load and frictional coefficient model are investigated. Second, the hybrid system theory is applied to provide state transfer condition for mode switching strategy. Finally, the control strategy that utilizes regenerative braking torque to compensate for difference of the required braking torque is designed, and a new μ‐H∞ control algorithm through D‐K iteration is presented to improve the robust performance. The proposed μ‐H∞ control strategy is examined under various braking situations, and the results indicate that (1) the μ‐H∞ controller have the advantage of robustness performance, the amplitude of regenerative braking is decreased by 6.14%, and the steady‐state error of hydraulic braking is decreased by 5.26% over the H∞, and (2) under the braking mode switching, the designed compensation control strategy has the performance of fast and accurate tracking of the desired torque, and the steady‐state error does not exceed 3.5%.

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“…Assuming that the stator windings are uniformly distributed and air gap magnetic field is trapezoidal distribution, the phase voltage equation can be calculated as following [25,26]:…”

Section: Hub-wheel Motor Modelmentioning

confidence: 99%

“…Assuming that the stator windings are uniformly distributed and air gap magnetic field is trapezoidal distribution, the phase voltage equation can be calculated as following [25, 26]:

([], \begin{array}{l} u_{a} \\ u_{b} \\ u_{c} \end{array}) goodbreak= ([], \begin{array}{c} R & 0 & 0 \\ 0 & R & 0 \\ 0 & 0 & R \end{array}) ([], \begin{array}{l} i_{a} \\ i_{b} \\ i_{c} \end{array}) goodbreak+ ([], \begin{array}{c} L_{s} & 0 & 0 \\ 0 & L_{s} & 0 \\ 0 & 0 & L_{s} \end{array}) \frac{d}{dt} ([], \begin{array}{l} i_{a} \\ i_{b} \\ i_{c} \end{array}) goodbreak+ ([], \begin{array}{l} e_{a} \\ e_{b} \\ e_{c} \end{array})

where

u_{a, b, c}

are expressed as stator phase voltage, respectively, V ;

i_{a, b, c}

are indicated as stator phase current, respectively, A ;

e_{a, b, c}

are shown as counter electromotive force, respectively, V ; R is the stator phase resistance, Ω;

L_{s}

is the equivalent inductance, H .…”

Section: Mathematical Model Of Research Objectmentioning

confidence: 99%

A μ‐H_∞ control strategy for decreasing torque fluctuation of electro‐hydraulic integrated braking system in mode switching

Zhang

Zhao

Wang

et al. 2023

Asian Journal of Control

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“…The WEKM algorithm proposed by Mendel et al is based on numerical integration to perform the weighted calculation of EKM. After simulation comparison, the calculation accuracy can be improved without affecting the speed [30]; based on Mendel research, CHEN et al proposed GT2 FLS' WNT TR method; it is proved through simulation that the combination of numerical integration method with NT algorithm can also effectively improve the accuracy of the fuzzy defuzzification [31][32][33]. This provided inspiration to the author and constructed the WTNT reduction algorithm based on the compound trapezoidal rule in IT2FLC and applied it to the physical experimental system for the first time.…”

Section: Wtnt Tr Algorithmmentioning

confidence: 99%

Dynamic High-Type Interval Type-2 Fuzzy Logic Control for Photoelectric Tracking System

Qin

Zhang²,

Zhao³

et al. 2022

Processes

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This paper proposes a dynamic high-type control (DHTC) method based on an interval type-2 fuzzy logic controller (IT2FLC), which is used in the photoelectric tracking system to improve the steady-state accuracy and response speed. Adding integrators to the traditional multi-loop feedback control loop can increase the system type, thereby speeding up the response speed and improving the steady-state accuracy, but there is a risk of integral saturation. Switching the type dynamically according to the system state can avoid integral saturation while retaining the advantages of the high-type. Fuzzy logic control (FLC) can dynamically change the output value according to the input change and has the advantages of fast response speed and strong ability to handle uncertainties. Therefore, in this paper, the FLC is introduced into the high-type control system, and the output of the FLC is used as the gain of the integrator to control the on-off to achieve the goal of dynamic switching type, which is successfully verified in the experiment. IT2FLC introduces a three-dimensional membership function, which further improves the FLC’s ability to handle uncertainties. From the experimental results, compared with T1FLC, IT2FLC’s ability to handle uncertainties is significantly improved. In addition, in order to speed up the calculation speed of IT2FLC, this paper proposes an improved type-reduction algorithm, which is called weighted-trapezoidal Nie-Tan (WTNT). Compared with the traditional type-reduction algorithm, WTNT has faster calculation speed and better steady-state accuracy, and has been successfully applied to real-time control systems, which has good engineering application value. Finally, in order to reduce the interference of human factors and improve the automation level of the system, a multi-population genetic algorithm (MPGA) is used to iteratively optimize the parameters of the FLC, which improves the output accuracy. On the experimental platform of the flexible fast steering mirror (FFSM), the control effects of the traditional controller, T1FLC and IT2FLC are compared, which proves that the IT2FLC-DHTC system has a faster response performance, higher steady-state accuracy, and stronger ability to handle uncertainties.

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“…The researchers use the full vehicle model to study ABS and further restore the actual use of ABS. Lv et al [18] developed an electrohydraulic composite ABS and its corresponding braking torque distribution strategy for electric vehicles based on interval type-2 fuzzy logic control strategy with a seven-degree-of-freedom whole-vehicle dynamics model, obtaining a better slip ratio control effect and excellent robustness. Fu [19] proposed a variable domain fuzzy PID control strategy based on particle swarm optimization algorithm on a seven-degree-of-freedom vehicle model to safeguard the braking effect of automotive ABS.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Vehicle Antilock System Based on Adaptive Neural Network Sliding Mode Control

Li,

2024

Mathematical Problems in Engineering

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Vehicle antilock systems play a very important role in the stability and reliability during vehicle braking. Due to the complexity of the braking process, antilock braking system (ABS) usually face the problems such as nonlinearity, time-varying, and uncertain parameter modeling. Thus, aiming at the parameter model uncertainty problem of ABS, an adaptive neural network sliding mode controller (ADRBF-SMC) is designed in this paper. On this basis, establishing the quarter-vehicle model and the seven-degree-of-freedom vehicle model, and treating the difference between the two models as a kind of disturbance, carrying out vehicle braking performance simulation experiments to analyze the variation of braking performance parameters such as vehicle and wheel speeds, slip ratio, braking distance, braking torque, under the three cases of adaptive neural network sliding mode controller, traditional sliding mode controller, and no control. Simulation results show that the adaptive neural network sliding mode controller (ADRBF-SMC) proposed in this paper can play an effective control role in both vehicle dynamics models. In addition, the control method proposed in this paper has stronger anti-interference capability and higher robustness compared with the sliding mode controller (SMC).

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Interval Type-2 Fuzzy Logic Anti-Lock Braking Control for Electric Vehicles under Complex Road Conditions

Cited by 12 publications

References 32 publications

A μ‐H_∞ control strategy for decreasing torque fluctuation of electro‐hydraulic integrated braking system in mode switching

A μ‐H_∞ control strategy for decreasing torque fluctuation of electro‐hydraulic integrated braking system in mode switching

Dynamic High-Type Interval Type-2 Fuzzy Logic Control for Photoelectric Tracking System

A Study on the Vehicle Antilock System Based on Adaptive Neural Network Sliding Mode Control

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Interval Type-2 Fuzzy Logic Anti-Lock Braking Control for Electric Vehicles under Complex Road Conditions

Cited by 12 publications

References 32 publications

A μ‐H∞ control strategy for decreasing torque fluctuation of electro‐hydraulic integrated braking system in mode switching

A μ‐H∞ control strategy for decreasing torque fluctuation of electro‐hydraulic integrated braking system in mode switching

Dynamic High-Type Interval Type-2 Fuzzy Logic Control for Photoelectric Tracking System

A Study on the Vehicle Antilock System Based on Adaptive Neural Network Sliding Mode Control

Contact Info

Product

Resources

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A μ‐H_∞ control strategy for decreasing torque fluctuation of electro‐hydraulic integrated braking system in mode switching

A μ‐H_∞ control strategy for decreasing torque fluctuation of electro‐hydraulic integrated braking system in mode switching