Cascade Modular Path Following Control Strategy for Gantry Virtual Track Train: Time-Delay Stability and Forward Predictive Model

Leng, Han; Ren, Lihui; Ji, Yuanjin

doi:10.1109/tvt.2022.3167921

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…Different from [33], the CG generalized force model of each module in the SRT is not identical due to the various actuator configurations. According to the CG generalized force model (28), in each control step, the wheel state control allocation (CA) of the i th module can be sorted into the convex QP problem [37][38][39] under the linear constraints of the wheel steering angle and torque:…”

Section: Control Allocation and "Virtual Axle" Methodsmentioning

confidence: 99%

“…Moreover, thanks to the proposed cascaded control modules, the controller can quickly expand based on the number of marshalling, and the effectiveness of the control method was verified through a theoretical model. Leng [28] proposed a scalable cascade modular path following a control strategy for the gantry virtual track train (G-VTT) based on a preview and tracking controller focusing on the lateral control for low-speed turning maneuvers, and the lag time was considered. Feng [29] introduced the path-tracking control of the ART.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Control Allocation and "Virtual Axle" Methodsmentioning

confidence: 99%

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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“…Virtual track is a target trajectory relative to traditional steel rails, which is achieved through laying magnetic nails on the road or drawing encoded graphics, combined with vehicle visual perception and magnetic induction technology, to achieve the same load-bearing, guiding, and constraint functions as tangible tracks. Virtual track trains utilize path tracking control technology to achieve high-precision autonomous tracking of target paths by controlling the wheel angles of each axle and the driving torque of the drive wheels [4,5]. Virtual track trains have operational characteristics such as dedicated right-of-way, high-speed dragging driving in straight sections (40-70 km/h), i.e., rear axles locking without steering, lowspeed controlled driving in curved sections (10-30 km/h), i.e., full wheel steering, and no emergency lane change.…”

Section: Introductionmentioning

confidence: 99%

Damping Coefficient Optimization for the Articulated System of Virtual Track Trains

Li,

Ji,

Huang

et al. 2024

Shock and Vibration

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Virtual track trains are a new type of rail transportation because the multisection formation structure leads to more degrees of freedom of the vehicle, which may cause unstable phenomena, such as tailing, cross-swing, and folding, affecting the stability and ride comfort of the vehicle driving. To explore the effect of damping coefficient of the articulated systems on vehicle dynamics performance, a vehicle system dynamics model is established based on the actual parameters of a three-module six-axle virtual track train. According to ISO14791: 2000, select typical working conditions such as straight line, lane change, and 1/4 circle curve, and optimize the damping coefficient of the articulated systems through co-simulation. The study shows that under straight-line conditions, increasing the damping coefficient can effectively suppress the yaw angular acceleration and improve the lateral ride comfort of the vehicle but has little effect on the vertical ride comfort. Under lane change conditions, too large or small damping coefficients will deteriorate the train’s lateral stability, and a reasonable damping coefficient will improve the yaw damping ratio of the vehicle and reduce the lateral sway vibration between vehicles. Under the 1/4 circle curve conditions, the additional articulated system damper will reduce the vehicle’s curve passing performance. In this paper, the articulation stability of multimodule fully connected vehicles is analyzed and optimized for the first time, and the damping coefficient control strategy is given based on the geometric tracking control method. The research results are of great significance for the parameter selection of virtual track trains’ articulated system and the design and development of specialized articulated systems for related vehicles.

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“…Leng [21] proposed the optimal acceleration tracking control method for a gantry virtual track train (GVTT) with a locking mechanism between vehicle modules. A local tracking objective function was used to calculate the optimal acceleration and steering angles.…”

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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Cascade Modular Path Following Control Strategy for Gantry Virtual Track Train: Time-Delay Stability and Forward Predictive Model

Cited by 8 publications

References 23 publications

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

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

Damping Coefficient Optimization for the Articulated System of Virtual Track Trains

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

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