A Novel Fuzzy Observer-Based Steering Control Approach for Path Tracking in Autonomous Vehicles

Zhang, Changzhu; Hu, Jinfei; Qiu, Jianbin; Yang, Weilin; Sun, Hong; Chen, Qijun

doi:10.1109/tfuzz.2018.2856187

Cited by 128 publications

(108 citation statements)

References 51 publications

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“…Although some changes are introduced to cancel out the influence caused by the ignorance in the kinematic model, there still exists a gap between the geometric model and the real bus, especially in high-speed and large curvature conditions. The k in Equation (9) is used to eliminate the error between proposed model with the real bus: As shown in Figure 9, the membership function is gaussmf type [6]. Both the inputs and outputs are qualitative into seven sets, denoted as NB, NM, NS, ZO, PS, PM, and PB, where NB is negative big, and PB is positive big.…”

Section: Pure Pursuit Controller With a Front Axle Referencementioning

confidence: 99%

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Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference

Yan

Kuang

et al. 2019

Applied Sciences

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Currently, since the model of a driverless bus is not clear, it is difficult for most traditional path tracking methods to achieve a trade-off between accuracy and stability, especially in the case of driverless buses. In terms of solving this problem, a path-tracking controller based on a Fuzzy Pure Pursuit Control with a Front Axle Reference (FPPC-FAR) is proposed in this paper. Firstly, the reference point of Pure Pursuit is moved from the rear axle to the front axle. It relieves the influence caused by the ignorance of the bus’s lateral dynamic characteristics and improves the stability of Pure Pursuit. Secondly, a fuzzy parameter self-tuning method is applied to improve the accuracy and robustness of the path-tracking controller. Thirdly, a feedback-feedforward control algorithm is devised for velocity control, which enhances the velocity tracking efficiency. The proportional-integral (PI) controller is indicated for feedback control, and the gravity acceleration component in the car’s forward direction is used in feedforward control. Finally, a series of experiments is conducted to illustrate the excellent performances of proposed methods.

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Section: Pure Pursuit Controller With a Front Axle Referencementioning

confidence: 99%

“…One of the most basic and key technologies in driverless buses is path tracking [4]. A path-tracking controller is applied to control the driverless bus to follow the desired path and velocity [5,6]. Generally, path tracking algorithms are divided into lateral and longitudinal control.…”

Section: Introductionmentioning

confidence: 99%

Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference

Yan

Kuang

et al. 2019

Applied Sciences

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“…Information about the state error (i.e. the lateral and yaw angle errors), as shown in Figure 5 and equations (7) and (8), is necessary for the design of a steering control algorithm.…”

Section: Error Dynamics For Steering Controlmentioning

confidence: 99%

Model predictive control–based steering control algorithm for steering efficiency of a human driver in all-terrain cranes

Seo

Noh

2019

Advances in Mechanical Engineering

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All-terrain cranes with multi-axles have large inertia and long distances between the axles that lead to a slower dynamic response than normal vehicles. This has a significant effect on the dynamic behavior and steering performance of the crane. Therefore, the purpose of this study is to develop an optimal steering control algorithm with a reduced driver steering effort for an all-terrain crane and to evaluate the performance of the algorithm. For this, a model predictive control technique was applied to an all-terrain crane, and a steering control algorithm for the crane was proposed that could reduce the driver's steering effort. The steering performances of the existing steering system and the steering system applied with the newly developed algorithm were compared using MATLAB/Simulink and ADAMS with a human driver model for reasonable performance evaluation. The simulation was performed with both a double lane change scenario and a curved-path scenario that are expected to happen in road-steering mode.

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“…Due to the higher requirements of the nonlinear performance of CAB design, the controller with robustness and fault-tolerance stands out and becomes the main direction of ABS research. For instance, a fuzzy observer-based steering control method is proposed by Zhang [9], which uses the T-S fuzzy control [10] to build the fuzzy vehicle dynamics model. Wang et al [11] described the adaptive robustness online constructive fuzzy control, which combines the adaptive decoupling function to improve the robustness performance.…”

Section: Introductionmentioning

confidence: 99%

“…A summary of several drawbacks of the recent popular anti-lock brake controllers (CABs) [5,9,12] and shown in the following:…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Sliding Mode Wheel Slip Ratio Control for Smart Vehicle Anti-Lock Braking System

2019

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With the development of in-wheel technology (IWT), the design of the electric vehicles (EV) is getting much improved. The anti-lock braking system (ABS), which is a safety benchmark for automotive braking, is particularly important. Installing the braking motor at each fixed position of the wheel improves the intelligent control of each wheel. The nonlinear ABS with robustness performance is highly needed during the vehicle’s braking. The anti-lock braking controller (CAB) designed in this paper considered the well-known adhesion force, the resistance force from air and the wheel rolling friction force, which bring the vehicle model closer to the real situation. A sliding mode wheel slip ratio controller (SMWSC) is proposed to yield anti-lock control of wheels with an adaptive sliding surface. The vehicle dynamics model is established and simulated with consideration of different initial braking velocities, different vehicle masses and different road conditions. By comparing the braking effects with various CAB parameters, including stop distance, braking torque and wheel slip ratio, the SMWSC proposed in this paper has superior fast convergence and stability characteristics. Moreover, this SMWSC also has an added road-detection module, which makes the proposed braking controller more intelligent. In addition, the important brain of this proposed ABS controller is the control algorithm, which can be used in all vehicles’ ABS controller design.

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A Novel Fuzzy Observer-Based Steering Control Approach for Path Tracking in Autonomous Vehicles

Cited by 128 publications

References 51 publications

Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference

Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference

Model predictive control–based steering control algorithm for steering efficiency of a human driver in all-terrain cranes

Fuzzy Sliding Mode Wheel Slip Ratio Control for Smart Vehicle Anti-Lock Braking System

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