Modeling and path tracking for articulated steering vehicles

Dou, Fengqian; Liu, Wei; Huang, Yanjun; Liu, Li; Meng, Yu

doi:10.1109/cac.2017.8243715

Cited by 4 publications

(11 citation statements)

References 11 publications

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“…For this reason, the AERS is modeled with a bicycle model in this paper. Figure 4 shows the free-body diagram of the bicycle model for the AERS [9,25,26]. As shown in Figure 4, the vehicle model consists of the front and rear bides.…”

Section: Modeling Of Vehicle With Articulated Frame Steering 21 Vehicle Modelingmentioning

confidence: 99%

“…With the rotation matrix and Equation ( 5), the longitudinal and lateral velocities of the rear body are calculated from those of the front body, as given in Equation ( 6). A detailed derivation procedure from Equation ( 1) to Equation ( 6) can be found in the references [9,16,31]. Generally, motion equations are derived with respect to the front body when modeling a vehicle with AFS.…”

Section: Vehicle Modelingmentioning

confidence: 99%

“…To cope with this problem, the diesel engine in road sweepers was replaced with an electric motor and a battery. The second is autonomous driving, which we developed in order to maximize utilization in dedicated areas such as airports or public parks without a human driver [8][9][10]. For example, most passenger vehicles are utilized within 20% of their total lifecycle.…”

Section: Introductionmentioning

confidence: 99%

“…In view of control, path tracking and speed controls are the most important aspects [8]. Path tracking control means that the driver model, which generates the steering angle command, needs to follow a given path [9,10,[21][22][23]. The speed control ensures that the vehicle follows a given speed profile by means of engine throttle or motor torque commands [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…Path tracking control means that the driver model, which generates the steering angle command, needs to follow a given path [9,10,[21][22][23]. The speed control ensures that the vehicle follows a given speed profile by means of engine throttle or motor torque commands [8,9]. The pure pursuit method is implemented as the patch tracking controller in AERS, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Rollover Prevention Control for Autonomous Electric Road Sweeper

Yim

Kim

2021

Electronics

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This paper presents a method to prevent the rollover of autonomous electric road sweepers (AERS). AERS have an articulated frame steering (AFS) mechanism. Moreover, the heights of the center of gravity of the front and rear bodies are high. As such, they are prone to rolling over at low speeds and at small articulation angles. A bicycle model with a nonlinear tire model was used as a vehicle model for AERS. Using that vehicle model, path tracking and speed controllers were designed in order to follow a predefined path and speed profile, respectively. To check the rollover propensity of AERS, load transfer ratio (LTR) based the rollover analysis was completed. Based on the results of the analysis, a rollover prevention scheme was proposed. To validate the proposed scheme, a simulation was conducted using a U-shaped path under constant speed conditions. From the simulation, it was shown that the proposed scheme is effective in preventing AERS from rolling over.

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Section: Modeling Of Vehicle With Articulated Frame Steering 21 Vehicle Modelingmentioning

confidence: 99%

Section: Vehicle Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rollover Prevention Control for Autonomous Electric Road Sweeper

Yim

Kim

2021

Electronics

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An unscented Kalman filter based velocity estimation method for articulated steering vehicle using a novel dynamic model

Yang

Zhu

Liu

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part K:

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This paper presents a novel velocity estimation method for an articulated steering vehicle to improve the automation and safety of articulated steering vehicle. The estimation method process is done in three steps: First, the articulated steering vehicle's tire forces are calculated separately against articulated steering vehicle's asymmetric tire load transfers. Then the tire forces are used to build a concise dynamic model, which only needs to consider the single centroid's dynamics in a fixed direction of the vehicle body. Finally, an unscented Kalman filter estimator is developed further based on the articulated steering vehicle dynamics model for velocity estimation. The main contribution of this paper to the related literature lies in two aspects. On the one hand, by converting the velocity vectors of each vehicle body to a fixed coordinate system, a concise dynamic model is built to avoid complex calculations and high sensor costs. On the other hand, an additional moment equilibrium equation is introduced to assist the simplified estimator in calculating the asymmetric tire dynamics of articulated steering vehicle, which ensures the accuracy and universality of the method. The simulation results from ADAMS/MATLAB indicate that the proposed model can effectively reflect the dynamics of articulated steering vehicle even under obvious load transfer. In addition, the developed unscented Kalman filter estimator can obtain accurate and robust estimation results in several typical vehicle manoeuvres.

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Integrated Vehicle Controller for Path Tracking with Rollover Prevention of Autonomous Articulated Electric Vehicle Based on Model Predictive Control

Jeong

2023

Actuators

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This paper presents an integrated controller for an autonomous articulated electric vehicle (AAEV) for path tracking and rollover prevention. The AAEV is vulnerable to rollover due to the characteristics of the articulated frame steering (AFS) mechanism, which shows improved maneuverability and agility but not front wheel steering. In addition, the ratio between height and track width is high, so the AAEV is prone to rolling over. Therefore, the proposed controller was designed to achieve the two goals, following the reference path and managing the velocity to improve the safety of the AAEV. Vehicle behavior was modeled by a kinematic model with actuation delay. A local linearization was used to improve the accuracy of the vehicle model and reduce the computational load. Reference states of the position and heading were determined to follow the reference path and prevent the rollover. A model predictive control (MPC)-based reference state tracker was designed to optimize the articulation angle rate and longitudinal acceleration commands. The simulation study was conducted to evaluate the proposed algorithm with a comparison of the base algorithms. The reference path for the simulation was an S-shaped path with discontinuous curvature. Simulation results showed that the proposed algorithm reduces the path tracking error and load-transfer ratio.

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Modeling and path tracking for articulated steering vehicles

Cited by 4 publications

References 11 publications

Rollover Prevention Control for Autonomous Electric Road Sweeper

Rollover Prevention Control for Autonomous Electric Road Sweeper

An unscented Kalman filter based velocity estimation method for articulated steering vehicle using a novel dynamic model

Integrated Vehicle Controller for Path Tracking with Rollover Prevention of Autonomous Articulated Electric Vehicle Based on Model Predictive Control

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