Differential-steering based path tracking control and energy-saving torque distribution strategy of 6WID unmanned ground vehicle

Jiang, Yue; Meng, Hao; Chen, Guanpeng; Yang, Congnan; Xu, Xiaojun; Zhang, Lei; Xu, Haijun

doi:10.1016/j.energy.2022.124209

Cited by 22 publications

(11 citation statements)

References 37 publications

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“…Based on HC theory and hierarchical kinematic model [ 30 , 31 , 32 , 33 ], we propose a distributed unmanned ground vehicle coordination control strategy, which is divided into two layers. In the upper layer control, the upper layer kinematic model is used as the prediction model of MPC, and the solution problem of future control increment is converted to the optimal solution problem of quadratic programming by setting the optimal objective function and constraint conditions.…”

Section: Control Designmentioning

confidence: 99%

Research on Six-Wheel Distributed Unmanned Vehicle Path Tracking Strategy Based on Hierarchical Control

et al. 2022

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For the multi-objective control problem of tracking effect and vehicle stability in the path tracking process of six-wheel distributed unmanned vehicles, a control strategy based on hierarchical control (HC) theory is proposed. A hierarchical kinematic model is designed considering the structural advantages of independent steering and independent driving of the unmanned vehicle, and this model is applied to the path tracking strategy. The strategy is divided into two levels of control. The upper level of control is to use the upper-level kinematic model as the prediction model of model predictive control (MPC), and to convert the solution problem of future control increments into the optimal solution problem of quadratic programming by setting the optimal objective function and constraints. The lower level of control is to map the optimal control quantities obtained from the upper level control to the six-wheel speeds and the four-wheel turning angles through the lower-level kinematics, and to design the six-wheel torque distribution rules based on deterministic torque and stability-based slip rate control for executing the control requirements of the upper level controller to prevent the unmanned vehicle from generating sideslip and precisely generating transverse moment to ensure the stable driving of the unmanned vehicle. Experiments were conducted on the Trucksim/Simulink simulation platform for a variety of road conditions, and the results showed that hierarchical control improved the accuracy of tracking the desired path and the driving stability on complex road surfaces more than MPC.

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Section: Control Designmentioning

confidence: 99%

Research on Six-Wheel Distributed Unmanned Vehicle Path Tracking Strategy Based on Hierarchical Control

et al. 2022

Self Cite

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“…Currently, many researchers have proposed several control theories and methods, such as fuzzy control, sliding mode variable structure control (SMC), model predictive control (MPC) [ 9 ] reinforcement learning [ 10 ], and other algorithms. Ying adopts the fuzzy control method for the controller of 4MIDEV to generate optimal regenerative braking torque to improve safety and economy during vehicle deceleration [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…Although fuzzy control has strong robustness and does not need an accurate mathematical model, its simple fuzzy processing of information will lead to the reduction of system control accuracy and poor dynamic quality, and the stability of electric vehicles is an important factor to measure the dynamic performance of vehicles, so fuzzy control is not suitable for this study. Yue used a model-free adaptive sliding mode control in the upper controller to estimate the required yaw moment, and in the lower controller, Yue used the seeker optimization algorithm (SOA) for torque distribution, which ensured the stability and energy-saving characteristics of the vehicle [ 9 ]. Sliding mode control can overcome the uncertainty of the system, and has strong robustness to disturbance and modeling dynamics, especially for the control of nonlinear systems.…”

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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“…A PID (Proportion Integration Differentiation) algorithm can control the vehicle lateral error according to the trajectory deviation, but the adjustment of parameters under different working conditions is the disadvantage of the algorithm [3][4][5]. A linear quadratic regulator (LQR) takes into account the influence of the vehicle dynamics model, but requires higher accuracy of the vehicle model [6][7][8][9][10]. Lghani Menhour [11] proposed a mathematical driver model based on a two-degrees-of-freedom PID multi-controller, developed a mathematical driver model, and verified the robustness and stability of the control, which improved the control accuracy under the uncertainty of nonlinear and structured parameters.…”

Section: Introductionmentioning

confidence: 99%

Optimal Control Strategy of Path Tracking and Braking Energy Recovery for New Energy Vehicles

et al. 2022

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In order to further improve the stability of path tracking control and fuel economy of new energy vehicles, an optimal control strategy of path tracking and braking energy recovery is proposed. First, a model predictive controller is designed based on the three-degrees of freedom dynamics model of the vehicle according to the idea of hierarchical control, and a fuzzy yaw torque controller is established with the desired yaw velocity and side slip angle of the mass center as constraints. Second, at high-speed driving conditions, the executive layer of the component distributes the braking torque according to the braking energy recovery control strategy. Finally, the optimal control strategy of path tracking and braking energy recovery is verified by Carsim/Advisor/Simulink software under different driving speeds. The results show that the optimized control strategy can improve the tracking accuracy and driving stability of a vehicle with large curvature turning and further improve the fuel economy of new energy vehicles under the premise of meeting the control requirements.

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Differential-steering based path tracking control and energy-saving torque distribution strategy of 6WID unmanned ground vehicle

Cited by 22 publications

References 37 publications

Research on Six-Wheel Distributed Unmanned Vehicle Path Tracking Strategy Based on Hierarchical Control

Research on Six-Wheel Distributed Unmanned Vehicle Path Tracking Strategy Based on Hierarchical Control

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

Optimal Control Strategy of Path Tracking and Braking Energy Recovery for New Energy Vehicles

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