System identification modelling based on modification of all terrain vehicle (ATV) using wireless control system

Aras, Mohd Shahrieel Mohd; Zambri, Mohd Khairi Mohd; Azis, Fadilah Abdul; Rashid, Mohd Zamzuri Ab; Kamarudin, Muhammad Nizam

doi:10.15282/jmes.9.2015.11.0159

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…toward the front or rear of the machine), so that what is left is a simple formulation of machine dynamics. For discussions on lateral forces and moments not included in this model, see [17][18][19]. When the system is laid out in such a configuration, the dynamics are described simply by doing a force and torque balance on the chassis and wheel, respectively [20].…”

Section: Quarter-car Vehicle Modelmentioning

confidence: 99%

Longitudinal vehicle dynamics model for construction machines with experimental validation

Alexander¹,

Vacca²

2017

Int. J. Automot. Mech. Eng.

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Construction machines represent a particular set of difficulties when modelling their system dynamics. Due to their generally low velocities and unorthodox operating conditions, the standard modelling equations used to simulate the behaviour of highway vehicles can have a poor behaviour for these systems. This paper sets forth a vehicle model which is suitable for construction machines, which travel at low velocities and encounter significant external forces in daily operation. It then shows the work done in validating the machine model with experimental data. First, the overall vehicle dynamics are developed, including a model for the machine behaviour when pushing against a resistive force. Then, a wheel force generation model suitable for low-velocity systems is discussed. Finally, pertinent experimental results are presented. Two different model validation tests were run. Both tests generated results which were matched well by the simulation model. In fact, the model matches experimental data reasonably well for both roading and pushing conditions. This indicates that the modelling methods described in this work are appropriate for the modelling of low-velocity systems such as wheel loaders and other construction machinery.

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Section: Quarter-car Vehicle Modelmentioning

confidence: 99%

Longitudinal vehicle dynamics model for construction machines with experimental validation

Alexander¹,

Vacca²

2017

Int. J. Automot. Mech. Eng.

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“…Yaw motion was used to determine the ATV's stability. The ATV model was inferred using the MATLAB system identification toolbox based on the results of the experiment or yaw motion [13]. A vehicle's motion is greatly influenced by rolling resistance, especially when it is operating off-road.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Overview of Control Algorithms, Sensors, Actuators, and Communication Tools of Autonomous All-Terrain Vehicles in Agriculture

Etezadi,

Eshkabilov

2024

Agriculture

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This review paper discusses the development trends of agricultural autonomous all-terrain vehicles (AATVs) from four cornerstones, such as (1) control strategy and algorithms, (2) sensors, (3) data communication tools and systems, and (4) controllers and actuators, based on 221 papers published in peer-reviewed journals for 1960–2023. The paper highlights a comparative analysis of commonly employed control methods and algorithms by highlighting their advantages and disadvantages. It gives comparative analyses of sensors, data communication tools, actuators, and hardware-embedded controllers. In recent years, many novel developments in AATVs have been made due to advancements in wireless and remote communication, high-speed data processors, sensors, computer vision, and broader applications of AI tools. Technical advancements in fully autonomous control of AATVs remain limited, requiring research into accurate estimation of terrain mechanics, identifying uncertainties, and making fast and accurate decisions, as well as utilizing wireless communication and edge cloud computing. Furthermore, most of the developments are at the research level and have many practical limitations due to terrain and weather conditions.

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“…The work on tuning the weights of MPC are also done for better lateral and roll stability utilizing an extended bicycle model with a linear tire model with the help of evolutionary algorithms [21]. An extended MPC with differential evolution optimization algorithm is proposed to achieve lateral and roll dynamics [22] Other than MPC, Electronic Stability Control (ESC) and steer by wire method [23], wireless control system [24] are employed to improve the yaw stability. Authors in [25], designed an artificial neural network controller for the shock absorbers to decrease the impact of vibration on patient's body by decreasing the damping coefficient.…”

Section: Introductionmentioning

confidence: 99%

An adaptive model predictive control for coupled yaw and rollover stability of vehicle during corner maneuvers

Kumar,

Dheer,

Verma

2023

JMES

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This paper presents a control strategy to achieve yaw and roll stability by taking into account the physical interaction between the yaw and roll dynamics to prevent vehicle collisions in hilly or curved terrain. The mathematical model is formulated utilizing a roll dynamic model with a small tyre slip angle and a bicycle model with two degrees of freedom considering coupling of yaw and roll dynamics. An adaptive model predictive controller and a PID controller are included in the proposed control methodology so that a real-time scenario of variation in the longitudinal velocity and friction coefficient is considered. Stability limits are established based on the yaw rate, sideslip, and roll motions of the vehicle, taking into account the effects of the road angle. The friction coefficients of 0.4 and 0.8 are chosen for wet and dry road surfaces to show manoeuvrability and force the vehicle to avoid rollover condition. Using numerical simulations in Matlab R2022a, the effectiveness of the designed controller is assessed. A root mean square error (RMSE) is calculated for the proposed methodology for the evaluation of the performance and the values are obtained as 3.032 and 3.912 for friction coefficient of 0.8 for yaw rate and roll angle respectively. On comparing with the other methodology, it is found that the performance of the proposed method is better based on RMSE. Also, the fluctuations at the corners are removed and the variables are bound inside the stability limit, thus avoiding the vehicle from accidents in hilly areas. The robustness of the controller towards increasing the mass of the vehicle by 5% and 10% is found to be good.

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System identification modelling based on modification of all terrain vehicle (ATV) using wireless control system

Cited by 6 publications

References 13 publications

Longitudinal vehicle dynamics model for construction machines with experimental validation

Longitudinal vehicle dynamics model for construction machines with experimental validation

A Comprehensive Overview of Control Algorithms, Sensors, Actuators, and Communication Tools of Autonomous All-Terrain Vehicles in Agriculture

An adaptive model predictive control for coupled yaw and rollover stability of vehicle during corner maneuvers

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