Kinematics and Path Following Control of an Articulated Drum Roller

Bian, Yongming; Yang, Meng; Fang, Xiaojun; Wang, Xiahui

doi:10.1007/s10033-017-0102-8

Cited by 14 publications

(3 citation statements)

References 18 publications

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“…Based on the classic kinematics model of articulated vehicles, Corke et al also designed a controller using feedback linearization control [10]. Until 2019, there are still new controllers based on feedback linearization [11][12][13][14][15].…”

Section: Related Workmentioning

confidence: 99%

A New Path Tracking Method Based on Multilayer Model Predictive Control

et al. 2019

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At present, many path tracking controllers are unable to actively adjust the longitudinal velocity according to path information, such as the radius of the curve, to further improve tracking accuracy. For this problem, we propose a new path tracking framework based on model predictive control (MPC). This is a multilayer control system that includes three path tracking controllers with fixed velocities and a velocity decision controller. This new control method is named multilayer MPC. This new control method is compared to other control methods through simulation. In this paper, the maximum values of the displacement error and the heading error of multilayer MPC are 92.92% and 77.02%, respectively, smaller than those of nonlinear MPC. The real-time performance of multilayer MPC is very good, and parallel computation can further improve the real-time performance of this control method. In simulation results, the calculation time of multilayer MPC in each control period does not exceed 0.0130 s, which is much smaller than the control period. In addition, when the error of positioning systems is at the centimeter level, the performance of multilayer MPC is still good.

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Section: Related Workmentioning

confidence: 99%

A New Path Tracking Method Based on Multilayer Model Predictive Control

et al. 2019

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“…Controllers based on vehicle dynamics model and tire model are designed in Hindiyeh and Gerdes [12] and Goh et al [13] to realize path-following control under extreme conditions. In addition, a control law based on the Lyapunov function was designed by Bian et al [14] for the articulated drum roller and proved its effectiveness in experiments. Then, MPC is also increasingly favored by academia.…”

Section: Introductionmentioning

confidence: 99%

Parameter self‐learning feedforward compensation‐based active disturbance rejection for path‐following control of self‐driving forklift trucks

Xie

Song

et al. 2023

Asian Journal of Control

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In this paper, for self‐driving forklift, a path‐following framework based on composite disturbance rejection that combines a cascaded active disturbance rejection controller and an online estimator of model parameters is proposed on the basis of a geometry feedforward controller. The model‐based cascaded active disturbance rejection control (ADRC), in particular, is composed of an outer‐loop controller and an inner‐loop controller, with the outer‐loop controller reducing lateral error by manipulating the desired heading direction and the inner‐loop controller achieving heading control by adjusting the steering angle. The unmodeled dynamics of the actual forklift motion are denoted as total disturbances, which can be timely estimated by the extended state observer (ESO). Moreover, to increase the transient response, a parameter self‐learning based geometry feedforward controller is designed in conjunction with the forklift characteristics. The stability analysis is presented and the effectiveness of the composite algorithm is quantitatively evaluated in experiments, demonstrating the superiority over the conventional pure pursuit algorithm and the only use of ADRC.

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“…To balance the lateral error and orientation error, Tan et al 25 designed a fusion deviation feedback controller based on a dynamic model. To tackle the sideslip issue in vibration operating mode, Fang et al 26 and Bian et al 27 established a path-following controller based on a roller vibration model. For improved performance over wide vehicle speeds, Jujnovich and Cebon 28 designed a gain scheduling active steering control strategy for articulated vehicles.…”

Section: Introductionmentioning

confidence: 99%

Layered disturbance rejection path-following control with geometry-based feedforward for unmanned rollers

Xie

Quanzhi

Song

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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As a widely used engineering vehicle, drum rollers have a higher degree of freedom in motion than conventional passenger vehicles. The uncertainties, caused by road and vehicle condition variations, introduce severe disturbances in the path-following control of unmanned rollers. In this work, a single-drum roller is considered, for which a composite disturbance rejection controller (CDRC) is proposed for path following. The CDRC comprises a disturbance rejection controller (DRC) and a modified pure pursuit controller (MPPC). The DRC lumps all the discrepancies of the models from the roller as total disturbance, which is estimated by the extended state observer (ESO) and rejected in the feedback control loop. For enhanced performance, MPPC is added as a feedforward controller, which calculates the target articulation angle based on the roller geometry model. Compared with the DRC, the settling time of the CDRC is reduced by 12.3%, and the lateral errors are reduced by 43.1% and 39.9% in the presence of uncertainties in the positioning system and steering motor, respectively, while it still maintains a low computational cost. The proposed controller have been used in 15 unmanned rollers for 2 years, using which over 5 million square meters area has been compacted.

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Kinematics and Path Following Control of an Articulated Drum Roller

Cited by 14 publications

References 18 publications

A New Path Tracking Method Based on Multilayer Model Predictive Control

A New Path Tracking Method Based on Multilayer Model Predictive Control

Parameter self‐learning feedforward compensation‐based active disturbance rejection for path‐following control of self‐driving forklift trucks

Layered disturbance rejection path-following control with geometry-based feedforward for unmanned rollers

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