Nonlinear Vehicle Dynamics and Trailer Steering Control of the TowPlow, a Steerable Articulated Snowplowing Vehicle System

The TowPlow is a novel type of snowplow that consists of a conventional snowplow vehicle and a steerable, plow-mounted trailer. The system is used to plow two typical traffic lanes simultaneously. In this paper, a nonlinear dynamic model of the TowPlow is developed for longitudinal, lateral, and yaw motions. The model considers nonlinearity through a modified Dugoff’s tire friction model, tire rotation dynamics, and quasi-static load transfer. The model is verified through steady-state and transient tests on an actual TowPlow system. A new snow resistance model is developed to allow simulation of the TowPlow in snow clearing operations. Then, active steering control of the trailer axle is derived with the goal of improving safety and efficiency of the TowPlow. The comparison of the simulation results between the controlled system and the uncontrolled system for cornering, slalom, up and down hill, and split friction coefficient braking maneuvers clearly demonstrates the efficacy of active steering control for the trailer axle of the TowPlow.

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A new robust combined control system for improving manoeuvrability, lateral stability and rollover prevention of a vehicle

Proceedings of the Institution of Mechanical Engineers Part K Journal of Multi-body Dynamics ◽

10.1177/1464419319887818 ◽

2019 ◽

Vol 234 (1) ◽

pp. 198-213 ◽

Cited By ~ 2

Author(s):

Mohammad Amin Saeedi

Keyword(s):

Control System ◽

Dynamic Model ◽

Control Method ◽

Sliding Mode ◽

Lateral Stability ◽

Steering Control ◽

Nonlinear Dynamic Model ◽

Active Steering ◽

Combined Control ◽

Active Steering Control

This paper presents a new effective method in order to achieve an appropriate performance for a four-wheeled vehicle during different conditions. The main goal of the study is focused on the handling improvement and lateral stability increment of the vehicle using a robust combined control system. First, in order to increase the vehicle's manoeuvrability, an active steering control system is proposed based on the sliding mode control method and using the simplified dynamic model. The tracking of the desired values of the yaw rate and lateral velocity of the vehicle is the main purpose for using the controller. Also, in order for verifying the performance of the sliding mode controller, the linearization feedback control method is used to design the active steering control system. Moreover, to improve the directional stability of the vehicle, a new active roll control system is proposed. In this control system, the roll angle is considered as the state variable as well as the active anti-roll-bar is utilized as an actuator to generate the roll moment. Then, a 14-degrees-of-freedom nonlinear dynamic model of the vehicle validated using CarSim software is utilized. Afterward, the performance of the designed combined control system is investigated at various velocities. The simulation results confirm that the combined control system has an important effect on vehicle's manoeuvrability improvement and its lateral stability increment, especially during severe transient manoeuvre.

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Research on vehicle active steering control based on linear matrix inequality and hardware in the loop test scheme design and implement for active steering

Advances in Mechanical Engineering ◽

10.1177/1687814019892108 ◽

2019 ◽

Vol 11 (11) ◽

pp. 168781401989210 ◽

Cited By ~ 1

Author(s):

Guangfei Xu ◽

Peisong Diao ◽

Xiangkun He ◽

Jian Wu ◽

Guosong Wang ◽

...

Keyword(s):

Linear Matrix Inequality ◽

Model Uncertainty ◽

Control Method ◽

Steering System ◽

Linear Matrix ◽

Matrix Inequality ◽

Steering Control ◽

Sensor Noise ◽

Active Steering ◽

Active Steering Control

In the research process of automotive active steering control, due to the model uncertainty, road surface interference, sensor noise, and other influences, the control accuracy of the active steering system will be reduced, and the driver’s road sense will become worse. The traditional robust controller can solve the model uncertainty, pavement disturbance and sensor noise in the design process, but cannot consider the performance enough. Therefore, this article proposes an active steering control method based on linear matrix inequality. In this method, the model uncertainty, road interference, sensor noise, yaw velocity, and slip side angle tracking errors are all considered as constraint targets, respectively, so that the performance and robust stability of the active front steering system can be guaranteed. Finally, simulation and hardware in the loop experiment are implemented to verify the effect of active front steering system under the linear matrix inequality controller. The results show that the proposed control method can achieve better robust performance and robust stability.

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Active steering control of railway vehicles using linear quadratic Gaussian (LQG)

10.1117/12.784125 ◽

2007 ◽

Author(s):

Min-Soo Kim ◽

Yeun-Sub Byun ◽

Joon-Hyuk Park ◽

Won-Hee You

Keyword(s):

Steering Control ◽

Linear Quadratic ◽

Linear Quadratic Gaussian ◽

Railway Vehicles ◽

Active Steering ◽

Active Steering Control

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Active-steering control system based on human hand impedance properties

2010 IEEE International Conference on Systems, Man and Cybernetics ◽

10.1109/icsmc.2010.5642313 ◽

2010 ◽

Cited By ~ 4

Author(s):

Yoshiyuki Tanaka ◽

Yusuke Kashiba ◽

Naoki Yamada ◽

Takamasa Suetomi ◽

Kazuo Nishikawa ◽

...

Keyword(s):

Control System ◽

Human Hand ◽

Steering Control ◽

Active Steering ◽

Active Steering Control

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Active steering control for autonomous vehicles based on a driver-in-the-loop platform: A case study of collision avoidance

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651819847005 ◽

2019 ◽

Vol 233 (10) ◽

pp. 1422-1437

Author(s):

Keji Chen ◽

Xiaofei Pei ◽

Daoyuan Sun ◽

Zhenfu Chen ◽

Xuexun Guo ◽

...

Keyword(s):

Risk Assessment ◽

Model Predictive Control ◽

Collision Avoidance ◽

Predictive Control ◽

Autonomous Vehicle ◽

Steering Control ◽

Safe Driving ◽

Active Steering ◽

Active Steering Control

Leveraging the advancements in sensor and mapping technologies, the collision-free autonomous vehicle becomes possible in the future. In this article, a case study of collision avoidance by active steering control is presented and verified by a driver-in-the-loop platform. The proposed control system integrates a risk assessment algorithm and a hierarchical model predictive control approach to ensure a safe driving. First, a fuzzy logic is used to estimate the potential conflict. Besides, a nonlinear model predictive control is introduced in the upper layer of the model predictive controller to generate a collision-free trajectory. Furthermore, the lower layer determines the optimal steering angle based on the linear time-variant model predictive control to follow the replanning path. The performance of the controller has been evaluated in the real-time driver-in-the-loop test. The results show that the autonomous vehicle is able to avoid the collision with the surrounding vehicle that is operated by a real driver, and the performance of collision avoidance is improved by means of the risk assessment.

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