An intelligent multiple-articulated rubber-tired vehicle based on automatic steering and trajectory following method

Xiao, Lei; Wang, Ke; Zhou, Sheng; Ma, Sai

doi:10.1177/1077546321999187

Cited by 8 publications

(8 citation statements)

References 22 publications

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Section: Introductionmentioning

confidence: 99%

“…Maciej Marcin Michalek 24 proposed a cascade tracking control method for N-unit wheeled robots, which can expand rapidly according to the number of units, and the effectiveness is verified by the principal model. Feng and Xiao 25,26 introduced the path-tracking of ART. The tracking error is calculated by image processing, and two different control methods of the first unit based on preview PID and model predictive control (MPC) are proposed, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Research on traction guidance cooperative control of virtual track train based on path-tracking and articulation hinge forces optimisation

Wang,

2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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Virtual track train (VTT) is a new urban public transportation system that adopts rubber wheel and rail transit management mode. Its characteristics of all-wheel-driving all-axle-steering (AWDAAS) enable this multi-articulated vehicle to run along the target path (virtual track) with high precision. This paper proposes a new traction guidance cooperative control strategy aiming at path-tracking and hinge forces optimisation. Firstly, the vehicle dynamics model is established using the two-step method, which mainly includes the centre of gravity (CG) forces model between the wheel steering angles, torques and the generalised forces at every unit. Secondly, the vehicle multi-body dynamics model is built with the CG generalised forces as the control input and carries on the concrete modelling for the three-unit six-axle AWDAAS-VTT. This modelling method can satisfy the rapid expansion and reconstruction according to the different marshalling numbers and axles. Then the hinge forces are analysed and expressed quantitatively by vehicle state and control inputs. A traction guidance cooperative control strategy based on a hierarchical framework is proposed, which has mainly three parts: the cooperative distribution of path-tracking and longitudinal speed targets, the CG generalised forces calculation of every unit based on improved model predictive control (MPC) and Stanley algorithm, and the control allocation (CA) of wheel torque and angle. The control strategy is verified by Co-simulation. The results show that the proposed control strategy can achieve excellent tracking accuracy and effectively reduce hinge forces. In addition, the vehicle under cooperative control also has excellent curve passing stability and continuous curve adaptability. The control strategy has high robustness under different running speeds, curve radii and payloads. Even under the extreme condition of passing through the R20 m curve at 14 m/s, the maximum tracking error is less than 0.035 m.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on traction guidance cooperative control of virtual track train based on path-tracking and articulation hinge forces optimisation

Wang,

2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…A Time delay unit was added to the first-order inertial to overcome this problem, and it works. Peng and Xiao also proposed similar methods for ART aiming at optimizing the transient process by adding time delay or increasing the order of the inertial unit [13,14]. But the time delay predicted by the current speed may not work well when speed changes frequently, considering the amazing vehicle length.…”

Section: Introductionmentioning

confidence: 99%

All-Wheel Steering Tracking Control Method for Virtual Rail Trains with Only Interoceptive Sensors

Wang,

Zhang,

Wang

2024

WEVJ

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A virtual rail train (VRT) is a multi-articulated vehicle as well as a novel public transportation system due to its low economic cost, environmental friendliness and high transit capacity. Equipped with all-wheel steering (AWS) and a tracking control method, the super long VRT can travel on urban roads easily. This paper proposed a tracking control approach using only interoceptive sensors with high scene adaptivity. The kinematic model was established first under reasonable assumptions when the sensor configuration was completed simultaneously. A hierarchical controller consists of a front axle controller and a rear axle controller. The former applies virtual axles theory to avoid motion interference. The latter generates a first-axle reference path with path segmentation and a data updating method to improve storage and computational efficiency. Then, a fast curvature matching rear axles control method is developed with an actuator time delay considered. Finally, the proposed approach is verified in a hardware in loop (HIL) simulation under various situations with predefined evaluation standards, which shows better tracking performance and applicability.

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“…In addition, the current control strategy based on the kinematics model ignores the tire sideslip, resulting in low tracking accuracy at medium and high speeds. At the same time, the end-to-end control strategy based on the dynamics model is too complex, which is not conducive to the real-time deployment of the controller [32]. In addition, the VTT has multiple types of structure, and the current control strategy is not universal.…”

Section: Introductionmentioning

confidence: 99%

Research on Virtual Track Train Path-Tracking Control Based on Improved MPC and Hierarchical Framework: A Reconfigurable Approach

Wang

Wei

et al. 2023

Applied Sciences

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The virtual track train (VTT) is a new urban public transportation system that adopts all-axle steering and distributed drive. The Super autonomous Rail rapid Transit (SRT), as one of them, adopts a four-module six-axle structure. In response to its crucial problem, its path tracking, this article proposes a reconfigurable dynamic modeling method, which has two parts: a multi-body dynamics model with generalized forces at each module’s center of gravity (CG) as the input, and the CG generalized force model, which expresses the CG generalized forces generated by the wheel control inputs. Then, a path-tracking strategy is proposed based on the improved MPC and hierarchical framework. Firstly, the CG generalized forces of each module required for path tracking were calculated, and then the CG generalized force redistribution was performed and the “virtual axle” method was proposed. Finally, the wheel state of each module was allocated. This strategy reduces the complexity of each layer of the controller and it solves the problem of insufficient actuators in the middle two modules of the SRT. Finally, through a hardware-in-the-loop (HIL) real-time simulation and comparison with different control strategies, the control strategy’s effectiveness, adaptability, and robustness were verified.

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An intelligent multiple-articulated rubber-tired vehicle based on automatic steering and trajectory following method

Cited by 8 publications

References 22 publications

Research on traction guidance cooperative control of virtual track train based on path-tracking and articulation hinge forces optimisation

Research on traction guidance cooperative control of virtual track train based on path-tracking and articulation hinge forces optimisation

All-Wheel Steering Tracking Control Method for Virtual Rail Trains with Only Interoceptive Sensors

Research on Virtual Track Train Path-Tracking Control Based on Improved MPC and Hierarchical Framework: A Reconfigurable Approach

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