Takagi-sugeno fuzzy model predictive controller design for combining lane keeping and speed tracking of four wheels steering and four wheels drive electric vehicle

Bian, Chentong; Yin, Guodong; Zhang, Ning; Xu, Liwei

doi:10.1109/ccdc.2017.7979212

Cited by 10 publications

(14 citation statements)

References 9 publications

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“…As discussed previously, the proposed ISPFCE algorithm can be applied in combination with previously-investigated motion controllers. In the simulation, two motion controllers were designed for comparison, one based on fuzzy model predictive control [12] (Controller A) and one based on a linear quadratic regulator [40] (Controller B). The performances of these motion controllers with and without the proposed ISPFCE algorithm are presented for comparison.…”

Section: Numerical Simulationmentioning

confidence: 99%

“…In particular, lane keeping systems (LKSs) and cruise control are two fundamental technologies for intelligent vehicles. A number of studies have been performed in these critical areas [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. A nested proportional-integral-derivative (PID) algorithm with two closed loops was presented for lane keeping in [7].…”

Section: Introductionmentioning

confidence: 99%

“…In [23], a proportional-type space-learning control scheme was constructed for vehicle path tracking, and a stability analysis of the proposed scheme was also performed. In [12], to achieve guaranteed lane keeping and speed tracking performance, a motion controller was developed based on fuzzy model predictive control. In [13], to consider nonlinear vehicle dynamics, a cruise control algorithm was proposed using nonlinear model predictive control.…”

Section: Introductionmentioning

confidence: 99%

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Integrated Speed Planning and Friction Coefficient Estimation Algorithm for Intelligent Electric Vehicles

et al. 2019

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To improve the safety of intelligent electric vehicles and avoid side slipping on curved roads with changing friction coefficients, an integrated speed planning and friction coefficient estimation algorithm is proposed. With this algorithm, the speeds of intelligent electric vehicles can be planned online using estimated road friction coefficients to avoid lane departures. When a decrease in the friction coefficient is detected on a curved road with a large curvature, the algorithm will plan a low and safe speed to avoid side slipping. When a normal friction coefficient is detected, the algorithm will plan a higher speed for normal driving. Simulations using MATLAB and CarSim have been performed to demonstrate the effectiveness of the designed algorithm. The simulation results suggest that the proposed algorithm is applicable to speed planning on curved roads with changing friction coefficients.

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Section: Numerical Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrated Speed Planning and Friction Coefficient Estimation Algorithm for Intelligent Electric Vehicles

et al. 2019

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“…Due to outstanding maneuvering and acceleration performances, the 4WS4WD vehicles [1,2] have been received attentions from researchers and engineers in recent years. Major concerns are motion controls of these overactuated vehicles, e.g., lateral stability control [3][4][5][6][7] and autonomous path-following control [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Vehicle lateral control systems can help the drivers to change the lanes safely or assist them in performing evasive maneuvers.…”

Section: Introductionmentioning

confidence: 99%

Motion Control of a 4WS4WD Path-Following Vehicle: Further Studies on Steering and Driving Models

Zhang

Yang

Zhang

et al. 2021

Shock and Vibration

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Further research on motion control of a 4WS4WD path-following vehicle is carried out in this paper. Focuses are placed on understanding and testing the vehicle path-following control models developed previously at a deep level. Control models are in relation to parameters introduced, and the effects of these parameters are discovered. Control models are interpreted by dynamic simulation using a 3DOF vehicle model with three cases. Three kinds of planned paths are considered in these cases to test control performances, which include the straight, circular, and sinusoidal paths. Interesting dynamic results are obtained and analyzed qualitatively, e.g., various steering modes. Simulation studies are extended with consideration of a fine vehicle dynamic model established in CarSim and a complex path composed of straight and curved segments. Control models are examined in a complex problem, and results obtained show that they are validated with robustness in dynamic environment.

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“…Combined with inverse longitudinal vehicle model and adaptive regulation of model predictive control (MPC), the vehicle can make use of the engine brake torque for various driving conditions and avoid high frequency oscillations automatically. The T-S fuzzy model is used to track the vehicle longitudinal speed, and the non-linear part of the model is approximated by the fuzzy rules (Bian et al, 2017). Aiming at the longitudinal control problem of distributed drive electric vehicle under various operating modes, three typical control modes are designed by model predictive controller-based multi-model control system (Shi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A method for vehicle speed tracking by controlling driving robot

Wang

Chen

Zhang

2019

Transactions of the Institute of Measurement and Control

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Using a driving robot instead of a human driver to carry out a vehicle emission durability test can effectively improve the accuracy of test. In order to accurately control the speed tracking of driving robot under different working conditions, a speed tracking method of driving robot based on dynamic fuzzy neural network (DFNN) direct inverse control is proposed, which considers the self-learning of vehicle longitudinal performance. Firstly, the kinematics and dynamics models of the driving robot’s mechanical legs are established. Moreover, in order to coordinately control the multi-legs of the driving robot to track the vehicle speed, the longitudinal performance model of the controlled vehicle is established by using performance self-learning data and neural network algorithm. Then, to accurately control the movement of the mechanical leg, a direct inverse controller based on DFNN is designed. The output of direct inverse controller is dynamically compensated by closed-loop control of braking force and throttle opening. Finally, the speed tracking experiments and simulations are carried out by human driver, proportional-integral-derivative (PID) control, fuzzy control and the proposed method under different working conditions. The results show that the proposed method does not have the same large prediction error as human driver. At the same time, it can track the speed quickly in different switching conditions. The fluctuation of the tracking speed is small, and the tracking error remains within ±1km/h. The proposed method avoids the design of complex control law based on model and can coordinately control the multi-legs to complete the tracking of the target speed.

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Takagi-sugeno fuzzy model predictive controller design for combining lane keeping and speed tracking of four wheels steering and four wheels drive electric vehicle

Cited by 10 publications

References 9 publications

Integrated Speed Planning and Friction Coefficient Estimation Algorithm for Intelligent Electric Vehicles

Integrated Speed Planning and Friction Coefficient Estimation Algorithm for Intelligent Electric Vehicles

Motion Control of a 4WS4WD Path-Following Vehicle: Further Studies on Steering and Driving Models

A method for vehicle speed tracking by controlling driving robot

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