Coordinated Path Following Control of 4WID-EV Based on Backstepping and Model Predictive Control

Wang, Chenning; He, Ren; Jing, Zhecheng; Chen, Shijun

doi:10.3390/en15155728

Cited by 5 publications

(7 citation statements)

References 30 publications

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“…Simultaneously, the proposed block backstepping controller was compared with a human driver's control [36], and with the traditional LQR controller (Fig. 3) as described in [26] for the same double lane change tracking path. The weighted matrix used in LQR is selected as: Q= diag( [1,3,1,3]), R= 10.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…These simulations for tracking the 2DOF autonomous vehicle in the desired double lane change were conducted using the proposed block backstepping control method. Meanwhile, for comparative purposes, a similar simulation for tracking the 2DOF autonomous vehicle in the desired double lane change were conducted using the LQR controller [26] and the driver transfer function controller method.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…However, only simulations of a road of a long radius of curvature were conducted. [26] adopted the hierarchical control structure. In the upper controller, to follow the path with high accuracy, the path following error model was converted into the yaw rate tracking problem employing the backstepping approach.…”

Section: Introductionmentioning

confidence: 99%

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A Modified Block Backstepping Controller for Autonomous Vehicle Lane Changing

2023

IJIES

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In this paper, a modified block backstepping controller was formulated for double lane changing control of a two-degree-of-freedom (2DOF) autonomous vehicle. For a systematic procedure, a 5th-degree polynomial double lane change trajectory was created. Next, the state model dynamics of this underactuated 2DOF autonomous vehicle is presented, which was transformed into the block-strict feedback form. Thereafter, the block backstepping technique was implemented to achieve the control input (steering angle). For enhancing the steady state performance during the trajectory tracking, an integral action was included in the proposed controller. The stability of this controller was established through the Lyapunov theory. To investigate the effectiveness of this proposed controller, we compared it with a human driver's transfer function controller and LQR controller, in addition to the sliding mode controller. Simulation results were achieved using CarSim-Simulink. Later, this controller was implemented to track the same lane change path with a different road coefficient of friction. The supposed controller characteristic was further investigated by inspecting the motion of the vehicle when exposed to an idealized lateral step force disturbance. The simulation results indicated the power of modified block backstepping control and it has higher following accuracy, better yaw rate tracking, and stable front steering angle, at high speed and at high and low friction roads.

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Section: Simulation and Resultsmentioning

confidence: 99%

Section: Simulation and Resultsmentioning

confidence: 99%

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A Modified Block Backstepping Controller for Autonomous Vehicle Lane Changing

2023

IJIES

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“…However, when the state trajectory reaches the sliding mode surface, it is easy to generate buffeting, and buffeting is difficult to eliminate. Wang established a 2-DoF vehicle model and path following the error model to obtain the desired yaw rate through inversion, used MPC to track the desired yaw rate and additional yaw moment, and obtained the optimal front wheel steering angle and additional yaw torque to ensure the path following and vehicle stability of the whole vehicle [ 13 ]. Jing used MPC to coordinate the AFS and DYC systems to ensure vehicle stability and minimize energy consumption to reduce the large additional yaw moment of the vehicle under high-speed cornering conditions [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…Zhai adopted an adaptive two-layer energy-saving torque distribution algorithm in the lower controller and used the friction circle constraint as the constraint for judging whether to switch the algorithm to ensure a more stable and energy-saving steering operation of the vehicle [ 16 ]. References [ 13 , 14 , 15 , 16 ] all adopt the MPC control method. MPC has the advantages of good control effect and strong robustness, which can effectively overcome the uncertainty, nonlinearity, and parallelism of the process, and can easily handle various constraints in the controlled variables and the control variables.…”

Section: Introductionmentioning

confidence: 99%

Coordinated Optimal Control of AFS and DYC for Four-Wheel Independent Drive Electric Vehicles Based on MAS Model

Zhang

Wang

et al. 2023

Sensors

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The problem that it is difficult to balance vehicle stability and economy at the same time under the starting steering condition of a four-wheel independent drive electric vehicle (4WIDEV) is addressed. In this paper, we propose a coordinated optimal control method of AFS and DYC for a four-wheel independent drive electric vehicle based on the MAS model. Firstly, the angular velocity of the transverse pendulum at the center of mass and the lateral deflection angle of the center of mass are decoupled by vector transformation, and the two-degree-of-freedom eight-input model of the vehicle is transformed into four two-degree-of-freedom two-input models, and the reduced-dimensional system is regarded as four agents. Based on the hardware connection structure and communication topology of the four-wheel independent drive electric vehicle, the reduced-dimensional model of 4WIDEV AFS and DYC coordinated optimal control is established based on graph theory. Secondly, the deviation of the vehicle transverse swing angular velocity and mass lateral deflection angle from their ideal values is oriented by combining sliding mode variable structure control (SMC) with distributed model predictive control (DMPC). A discrete dynamic sliding mode surface function is proposed for the ith agent to improve the robustness of the system in response to parameter variations and disturbances. Considering the stability and economy of the ith agent, an active front wheel steering and drive torque optimization control method based on SMC and DMPC is proposed for engineering applications. Finally, a hardware-in-the-loop (HIL) test bench is built for experimental verification, and the results show that the steering angle is in the range of 0–5°, and the proposed method effectively weighs the system dynamic performance, computational efficiency, and the economy of the whole vehicle. Compared with the conventional centralized control method, the torque-solving speed is improved by 32.33 times, and the electrical consumption of the wheel motor is reduced by 16.6%.

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