Intelligent tractors: Review of online traction control process

Sunusi, Idris Idris; Zhou, Jun; Wang, Zhen Zhen; Sun, Changyin; Ibrahim, Ibrahim Eltayeb; Korohou, Tchalla; Soomro, Shakeel Ahmed; Sale, Nura A; Olanrewaju, T.O.

doi:10.1016/j.compag.2019.105176

Cited by 35 publications

(28 citation statements)

References 80 publications

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“…The semi-empirical models are computationally faster than the theoretical ones. They are more general in terms of applicability in comparison to purely empirical ones [8]. Because the tractor is designed to work on concrete, road, firm soil, and agricultural farms, a model with broad applicability as well as simplicity is required.…”

Section: Wheel Terrain Interaction and Optimum Slip Estimationmentioning

confidence: 99%

“…To leverage on the advantages of electric vehicle technologies, electric tractors were developed for pesticide applications and other agricultural purposes. The main advantages are that such operation does not require high power; as such, their power demand can be met by the electric batteries [3][4][5][6][7][8]. Though much progress has been achieved on improving batteries' performance, its energy density is still lower than that of conventional fuel, and the cost is still high [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Development of Online Adaptive Traction Control for Electric Robotic Tractors

et al. 2021

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Estimation and control of wheel slip is a critical consideration in preventing loss of traction, minimizing power consumptions, and reducing soil disturbance. An approach to wheel slip estimation and control, which is robust to sensor noises and modeling imperfection, has been investigated in this study. The proposed method uses a simplified form of wheels longitudinal dynamic and the measurement of wheel and vehicle speeds to estimate and control the optimum slip. The longitudinal wheel forces were estimated using a robust sliding mode observer. A straightforward and simple interpolation method, which involves the use of Burckhardt tire model, instantaneous values of wheel slip, and the estimate of longitudinal force, was used to determine the optimum slip ratio that guarantees maximum friction coefficient between the wheel and the road surface. An integral sliding mode control strategy was also developed to force the wheel slip to track the desired optimum value. The algorithm was tested in Matlab/Simulink environment and later implemented on an autonomous electric vehicle test platform developed by the Nanjing agricultural university. Results from simulation and field tests on surfaces with different friction coefficients (μ) have proved that the algorithm can detect an abrupt change in terrain friction coefficient; it can also estimate and track the optimum slip. More so, the result has shown that the algorithm is robust to bounded variations on the weight on the wheels and rolling resistance. During simulation and field test, the system reduced the slip from non-optimal values of about 0.8 to optimal values of less than 0.2. The algorithm achieved a reduction in slip ratio by reducing the torque delivery to the wheel, which invariably leads to a reduction in wheel velocity.

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Section: Wheel Terrain Interaction and Optimum Slip Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of Online Adaptive Traction Control for Electric Robotic Tractors

et al. 2021

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“…The slip rate is used to evaluate the traction wheel slippage that occurs during the vehicle driving process and represents one of the most significant parameters to influence the tractor's traction efficiency and power consumption [15,16] . For tractor traction control, Sunusi et al [4] summarized the process of and method for online traction control from a dynamics perspective. To ensure the quality of the operation, traction efficiency maximization and power minimization, slip rate control must be realized on the basis of the electro-hydraulic control.…”

Section: Study Of Tractor Slip Control Strategymentioning

confidence: 99%

“…Essentially, automated tractors are perceptive machines that can be programmed to power various types of agricultural implement [3] and can provide increased operating speeds while reducing downtime and eliminating human-related problems [4] . However, most previous research has indicated that incorporation of intelligent technology into agriculture has proved difficult because of the significant variations encountered in the different tasks to be performed, the structures of the implements used, and the operating environment (including ground surface conditions, temperature, humidity, dust, and mud) [1,5] .…”

Section: Introduction mentioning

confidence: 99%

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Review of electro-hydraulic hitch system control method of automated tractors

Wang¹,

Dong²,

Wang³

et al. 2021

International Journal of Agricultural and Biological Engineering

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The automatic and intelligent degree of tractor hitch system determines the operations quality, efficiency, tractor power output and energy consumption, and automatic control of electro-hydraulic hitch is one of the key technical problems in the realization of automated tractor. In this study, the adjustment methods and characteristics of electro-hydraulic hitch system were summarized. The development of electro-hydraulic hitch control technologies was elaborated and analyzed from both the electro-hydraulic control strategy and slip control strategy perspectives. The implementation methods and control characteristics were discussed. A new idea for the development of automated tractor was put forward that integrating cutting-edge technologies such as big data, fusion control and artificial intelligence. In addition, based on the precise and efficient control of electro-hydraulic hitch system, a high reliability of the control system of the tractor with intelligent application of the control algorithm was obtained. The results can provide a reference for the precise operation of automated tractors under the big data in agriculture.

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Deep neural networks to predict autonomous ground vehicle behavior on sloping terrain field

Badgujar

Das

Figueroa

et al. 2023

Journal of Field Robotics

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Conventional large agricultural machinery or implements are unsafe and unsuitable to operate on slopes > 6 ∘ $\gt {6}^{\circ }$ or 10%. Tractor rollovers are frequent on slopes, precluding farming on arable hills, uneven or highly sloped land. Therefore, a fleet of autonomous ground vehicles (AGV) is proposed to cultivate highly sloped land (> 6 ∘ $\gt {6}^{\circ }$). The fleet aims to expand agricultural land to the slopes and to strengths the human‐robot collaboration in an unsafe sloped environment. However, the fleet's success largely depends on vehicle behavior models regarding traction, mobility, and energy consumption on varying slopes. The vehicle intelligent behavior models are essential and would solve multiple objectives ranging from simulations to path planning & navigation. Therefore, this study aimed to build a deep learning‐based vehicle behavior models on sloping terrain. A standard drawbar test was performed on a single AGV operating on an actual sloped field at varying speeds and load conditions. The drawbar test quantified the AGV's behavior on slopes in metrics related to traction (traction efficiency), mobility (travel reduction), and energy consumption (power number). Deep learning‐based models were developed from the experimental data to predict the AGV's behavior on slopes as a function of vehicle velocity, drawbar, and slope. A special model called the proposed model, which combined multiple deep neural networks with a mixture of Gaussians, was developed and trained with a hybrid training method. The proposed model consistently outperformed the other well‐known machine learning models. This study explored the capabilities of machine learning algorithms to simulate the behavior of small‐track vehicle or AGV on sloping terrain. The fleet aims to provide safer agriculture keeping human safety in focus, and the developed predictive vehicle behavior models would empower the fleet's operation on currently unsafe sloped terrain by assisting in vehicle path planning, route optimization, and decision making.

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Intelligent tractors: Review of online traction control process

Cited by 35 publications

References 80 publications

Development of Online Adaptive Traction Control for Electric Robotic Tractors

Development of Online Adaptive Traction Control for Electric Robotic Tractors

Review of electro-hydraulic hitch system control method of automated tractors

Deep neural networks to predict autonomous ground vehicle behavior on sloping terrain field

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