Trajectory tracking for agricultural tractor based on improved fuzzy sliding mode control

Yin, Chenbo; Wang, Shourui; Gao, Jie; Li, Xiaowei

doi:10.1371/journal.pone.0283961

Cited by 5 publications

(5 citation statements)

References 40 publications

(36 reference statements)

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“…Sun et al [31] aim to devise a dynamic tractor model suitable for sloped terrains, introducing a robust control strategy anchored in sliding mode observers. Additionally, [32] investigates the trajectory tracking of agricultural tractors utilizing an enhanced fuzzy sliding mode control. This approach targets tractors equipped with electric power steering mechanisms.…”

Section: Related Workmentioning

confidence: 99%

Sliding Mode Controller for Autonomous Tractor-Trailer Vehicle Reverse Path Tracking

Bin Salamah

2023

Applied Sciences

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In the past few years, there has been a growing interest among researchers in developing control systems for autonomous vehicles, specifically for tractor-trailer systems. This newfound interest is driven by the potential benefits of enhancing safety, reducing costs, and addressing labor shortages in the industry. Two industries that could reap the rewards of these systems’ advancements are cargo and agriculture transportation. One of the challenging tasks for the truck trailer vehicle is driving in reverse. Backward path tracking of tractor-trailers is a complex control problem with practical applications. The difficulty in controlling the vehicle arises due to its unstable internal dynamics, coupled nonlinear terms, and the under-actuated nature of the system. There is also a limit to the angle at which the steering can be turned before the risk of a jackknife accident increases significantly. In response to these challenges, this paper introduces a robust sliding mode controller designed for path tracking in reverse-driving tractor-trailer systems. The novelty of our work lies in addressing these challenges, which have not been extensively studied in the past. The proposed controller is analyzed, and its performance is tested and verified using different scenarios. The simulation examples show superior control performance, and we anticipate that this novel controller holds the potential to be widely adopted as a fundamental component in the path-tracking algorithms of autonomous truck trailer systems.

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Section: Related Workmentioning

confidence: 99%

Sliding Mode Controller for Autonomous Tractor-Trailer Vehicle Reverse Path Tracking

Bin Salamah

2023

Applied Sciences

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“…In traditional trajectory planning, multidimensional trajectory problems can usually only be solved by solving multiple dimensions separately. Fliess et al first proposed the concept of differential flatness for nonlinear systems [24], and it was valued in the field of robotic trajectory planning in the following years [25][26][27]. The differential flatness property is conducive to lowering the dimensionality in the optimization problem, decreasing the number of operations, and increasing the efficiency of the computation.…”

Section: Differential Flatness-based Control Optimization Trajectoriesmentioning

confidence: 99%

“…Shi et al [23] designed a trajectory tracking controller for intelligent vehicles based on MPC and fuzzy control, and verified in simulation that this method has a good tracking effect under high-speed working conditions. Boryga et al [24] present a method of bicurve polynomial transition curve application for making agricultural aggregate movement paths during headland turn drives as well as within the field.…”

Section: Introductionmentioning

confidence: 99%

“…The uniqueness of the optimality condition determines the nonsingularity of the matrix M with respect to the time vector T whose elements are all positive, so that the unique optimal solution of the coefficient matrix c can be determined by the system of linear Equation (24). Thus, by means of the optimality condition and the system of Equation (24), and given the boundary conditions D 0 , D M and the intermediate conditions D i , it is possible to obtain the optimal polynomial trajectories with the optimal cost of control for unconstrained conditions without the aid of the cost function, and the computational complexity of the method is linear.…”

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confidence: 99%

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Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots

Li,

Hu,

et al. 2023

Applied Sciences

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Coal Mine Mobile Robots (CMRRs) are generally large in size and inertia, while narrow laneway space and bumpy terrain pose great challenges to CMRR’s planning and control. Aiming at the trajectory planning and tracking problems of CMRR, a new trajectory class MINCO is derived in detail based on the properties of differential flat systems. A trajectory planning method based on MINCO trajectory and safety corridor constraints constructed with underground environmental constraints is further proposed. A trajectory tracking method based on model predictive control (MPC) is further proposed. The prediction model of MPC is constructed by a kinematics model and transformed into a standard quadratic programming problem according to the cost function of a trajectory tracking target. Finally, large quantities of field tests were carried out for the proposed approaches. The results show that the proposed planning algorithm based on MINCO trajectory can achieve good avoidance effects within 10 planning attempts in different obstacle scenarios, and the trajectory is smoother compared to the Fast-Planner algorithm, with shorter trajectory length and less planning time. The tracking error of MPC is always less than 0.05 m in different underground scenarios, having a more adaptable trajectory tracking effect than PID.

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“…Modern smart agriculture [1][2][3][4][5][6], as the strategic development direction of national agricultural production, effectively combines information technology with traditional agricultural technology [7][8][9] to achieve the goal of refinement, efficiency, and greening of agricultural production. The coordinated development of agricultural machinery and equipment and agricultural informatization [10][11][12] is the key to promoting the development of modern smart agriculture, which not only changes the industrial model of traditional agriculture that must rely on environmental planting but also makes efficient use of various agricultural resources and improves the quality and yield of crops [13].…”

Section: Introductionmentioning

confidence: 99%

Real-time phenotyping measurement system for vegetable leaves

Huang,

Liu,

Cheng

et al. 2024

Preprint

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In the process of vegetable growth, leaf area phenotypic information measurement is an effective means to evaluate the growth status of vegetables. Due to the irregular leaf shape, the accuracy of leaf area measurement is low, and real-time field measurement is difficult to achieve. According to the field situation, a real-time phenotypic measurement system for vegetable leaves was designed. The vegetable leaves are collected in real-time by the detection vehicle, and the YOLACT segmentation model is used to realize the real-time measurement of vegetable leaves. By introducing the Res2Net module after adding the ECA attention mechanism into the model backbone network Resnet50, the network receptive field is expanded and the segmentation performance of the network on the leaves is improved. In the field experiment, the segmentation accuracy and detection accuracy of vegetable leaves reached 41.51% and 39.39%, respectively, and the segmentation speed was 23.10 frame/s. The results show that the designed real-time phenotypic measurement system of vegetable leaves not only meets the accurate area measurement of irregular vegetable leaves, but also realizes the real-time requirement of vegetable leaf area measurement, and improves the reliability of leaf phenotypic information to evaluate the growth status of vegetables.

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Trajectory tracking for agricultural tractor based on improved fuzzy sliding mode control

Cited by 5 publications

References 40 publications

Sliding Mode Controller for Autonomous Tractor-Trailer Vehicle Reverse Path Tracking

Sliding Mode Controller for Autonomous Tractor-Trailer Vehicle Reverse Path Tracking

Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots

Real-time phenotyping measurement system for vegetable leaves

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