Trajectory tracking of in-wheel motor electric vehicles based on preview time adaptive and torque difference control

Han, Wenyao; Li, Aijuan; Huang, Xin; Li, Wei; Cao, Jiaping; Bu, Haixiang

doi:10.1177/16878132221089909

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…If the vehicle can travel along a road, whose curvature is  , at the reference point, expected pal stance value of des  can be represented by Equation (9).…”

Section: Model Theoretical Algorithm and Simulation Test: 1 Vehicle-r...mentioning

confidence: 99%

Research of the LQR algorithm with constraints based on preview control

Yang

Ouyang

2023

Preprint

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This study proposes a lateral and longitudinal control strategy for intelligent vehicles based on the preview control theory and linear quadratic regulator (LQR) to enhance the trajectory tracking ability and stability. The two-degree-of-freedom dynamic model of the vehicle and the road-vehicle error dynamic model is established, and the future road curvature is incorporated as a disturbance into the LQR state vector using the preview control theory. An augmented LQR problem is solved according to the optimal theory to obtain the analytical solution of the control quantity. This strategy also enhances the adaptive ability of the intelligent vehicle to extreme conditions by taking into account the dynamic constraints. The preview time is optimized using the simulated annealing algorithm to obtain the optimal preview time under different vehicle speeds and road friction coefficients. The stability of the closed-loop control system composed of the new algorithm is analyzed to verify its feasibility. Simulation results on the Carsim/Simulink joint platform demonstrate that it has excellent trajectory tracking ability, stability, and robustness to the vehicle speed. The proposed strategy has the potential to significantly advance the field of intelligent vehicle control and improve the safety and efficiency of transportation systems.

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“…If the vehicle can travel along a road, whose curvature is  , at the reference point, expected pal stance value of des  can be represented by Equation (9).…”

Section: Model Theoretical Algorithm and Simulation Test: 1 Vehicle-r...mentioning

confidence: 99%

Research of the LQR algorithm with constraints based on preview control

Yang

Ouyang

2023

Preprint

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“…Since model predictive control is may not be very effective for nonlinear models, it is necessary to linearize such models. 41–45…”

Section: Model Predictive Controlmentioning

confidence: 99%

“…Since model predictive control is may not be very effective for nonlinear models, it is necessary to linearize such models. [41][42][43][44][45] The nonlinear dynamic model is linearized according to the deviation between the expected trajectory of the lane change and the actual driving state quantity of the vehicle, and the following equation is obtained:…”

Section: Model Predictive Controller Designmentioning

confidence: 99%

Research on the trajectory tracking of a curved road in an active lane change scenario based on model predictive control algorithm

Wang,

Qu,

Guo

et al. 2023

Advances in Mechanical Engineering

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In this paper, a control method for trajectory planning and tracking of an intelligent vehicle is proposed. In terms of trajectory planning, a trajectory planning method for curved lane changes is designed based on conventional lane change trajectory planning and considering the adaptive correction of road curvature. In addition, curve trajectory tracking control strategy based on model predictive control is designed. Model predictive control is suitable for multi-input and multi-output nonlinear models, and it has the advantage of considering model constraints. This type of control makes the model output more in line with vehicle dynamics characteristics and improves the trajectory tracking accuracy. Finally, the simulation shows that the method proposed in this paper can generate a reasonable curved lane-changing trajectory, and under the consideration of the vehicle dynamics constraints, the MPC algorithm is used to effectively follow the expected trajectory, so that the vehicle can change lanes smoothly.

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“…The trajectory tracking controller adopts the preview time adaptive MPC trajectory tracking control method designed in (Han et al, 2022). The nonlinear model predictive control (NMPC) needs to go through complex numerical calculations in the solution process.…”

Section: Trajectory Tracking Controller Designmentioning

confidence: 99%

Four-wheel-drive vehicle trajectory tracking control at joint planning layer

Han²,

Liu³

et al. 2022

Front. Energy Res.

Self Cite

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Because the real road conditions are complex and changeable, the vehicle cannot drive normally according to the predetermined trajectory. Therefore, for the four-wheel drive electric vehicle, a trajectory tracking control method of joint planning layer is proposed. First, in the process of vehicle trajectory tracking, the obstacle avoidance trajectory is planned for the dynamic obstacle environment. Then, an MPC trajectory tracking controller with a velocity planning module is designed to reduce the lateral acceleration of the vehicle avoiding obstacles at high speed during trajectory tracking. Finally, CarSim/Simulink co-simulation verification and hardware-in-the-loop (HIL) simulation verification are carried out to verify the performance of the controller. The simulation results show that the trajectory planning module can successfully plan the trajectory of obstacle avoidance, and the improved MPC controller can effectively reduce the lateral acceleration of the target vehicle during trajectory tracking; HIL analysis shows that the MPC trajectory tracker with speed planning module can control the vehicle with good driving stability. Trajectory tracking control under urban road conditions will be considered in future research.

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Trajectory tracking of in-wheel motor electric vehicles based on preview time adaptive and torque difference control

Cited by 9 publications

References 14 publications

Research of the LQR algorithm with constraints based on preview control

Research of the LQR algorithm with constraints based on preview control

Research on the trajectory tracking of a curved road in an active lane change scenario based on model predictive control algorithm

Four-wheel-drive vehicle trajectory tracking control at joint planning layer

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