An improved path-tracking controller with mid-angle adaptive calibration for combine harvester

et al. 2023

Applied Sciences

This paper provides a review of path-tracking strategies used in autonomous agricultural vehicles, mainly from two aspects: vehicle model construction and the development and improvement of path-tracking algorithms. Vehicle models are grouped into numerous types based on the structural characteristics and working conditions, including wheeled tractors, tracked tractors, rice transplanters, high clearance sprays, agricultural robots, agricultural tractor–trailers, etc. The application and improvement of path-tracking control methods are summarized based on the different working scenes and types of agricultural machinery. This study explores each of these methods in terms of accuracy, stability, robustness, and disadvantages/advantages. The main challenges in the field of agricultural vehicle path tracking control are defined, and future research directions are offered based on critical reviews. This review aims to provide a reference for determining which controllers to use in path-tracking control development for an autonomous agricultural vehicle.

Section: Dry Field Agricultural Machinerymentioning

confidence: 99%

Modelling and Control Methods in Path Tracking Control for Autonomous Agricultural Vehicles: A Review of State of the Art and Challenges

et al. 2023

Applied Sciences

“…Finally, the steering angle deviation acts on the actuator of harvester, thus realizing path tracking. It is worth mentioning that the integrated navigation system and the control of actuator have been researched in the literature (Noh et al , 2008; Qiao et al , 2020a), which are not discussed in this study.…”

Section: Adaptive Neural Network-based Path Tracking Control Designmentioning

confidence: 99%

“…For the control strategies, the first type is the controller based on geometric and kinematic model, which is by far the most popular type due to its simplicity and stability. The pure pursuit algorithm is the most widely used in this type (Iida et al , 2013; Qiao et al , 2020a). The key behind the pure tracking algorithm is setting the look-ahead distance, which is related to the working speed and tracking path curvature (Samuel et al , 2016; Wang et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural network-based path tracking control for autonomous combine harvester with input saturation

Zhang

Liu³

2021

Purpose To reduce the effect of parameter uncertainties and input saturation on path tracking control for autonomous combine harvester, a path tracking controller is proposed, which integrates an adaptive neural network estimator and a saturation-aided system. Design/methodology/approach First, to analyze and compensate the influence of external factors, the vehicle model is established combining a dynamic model and a kinematic model. Meanwhile, to make the model simple, a comprehensive error is used, weighting heading error and position error simultaneously. Second, an adaptive neural network estimator is presented to calculate uncertain parameters which eventually improve the dynamic model. Then, the path tracking controller based on the improved dynamic model is designed by using the backstepping method, and its stability is proved by the Lyapunov theorem. Third, to mitigate round-trip operation of the actuator due to input saturation, a saturation-aided variable is presented during the control design process. Findings To verify the tracking accuracy and environmental adaptability of the proposed controller, numerical simulations are carried out under three different cases, and field experiments are performed in harvesting wheat and paddy. The experimental results demonstrate the tracking errors of the proposed controller that are reduced by more than 28% with contrast to the conventional controllers. Originality/value An adaptive neural network-based path tracking control is proposed, which considers both parameter uncertainties and input saturation. As far as we know, this is the first time a path tracking controller is specifically designed for the combine harvester with full consideration of working characteristics.

“…This algorithm can make the balance car track the specified trajectory well under the premise of maintaining dynamic balance, having a faster dynamic response speed and good robustness to interference. The third group of research focuses on the design based on the geometric model, such as pure pursuit (PP) (Qiao et al , 2020) and Stanley (AbdElmoniem et al , 2020). These methods are suitable for low-speed field conditions, but the forward-looking distance has a greater influence.…”

Section: Introductionmentioning

confidence: 99%

An improved pure pursuit path tracking control method based on heading error rate

Chen

2022

Purpose In path tracking, pure pursuit (PP) has great superiority due to its simple control. However, when in agricultural applications, the performance and accuracy of PP are not so well; it cannot be tracked in time has slow convergence, and low tracking accuracy. Furthermore, in some severe driving scenarios, PP is insufficient to convey the effects of the tracking error. This paper aims to propose an autonomous driving controller to improve the PP model based on heading error rate (Improved PP-improved search strategy ant colony optimization [ISSACO]). Design/methodology/approach First, the heading error rate is added as the control method in the PP model. Second, the predicted heading error was selected as the objective function; the ISSACO is used to obtain the minimum value of the predicted heading error. A PP controller is integrated with the heading error rate by ISSACO to better deal with tracking error by trading off between PP and heading error rate. Third, the ISSACO was used to obtain the optimal values of PP and heading error rate weight. Finally, the error feedback adaptive dynamic adjustment of the improved algorithm is realized to reduce the convergence time and tracking error. Findings The proposed method was tested on a four-wheeled vehicle robot, and the effectiveness of its convergence was proved. Experiments show that the proposed method can effectively reduce the tracking error, increase convergence, then improve the robot’s working quality. Originality/value An adaptive improved PP path tracking control is proposed, which considers both heading error rate and parameter uncertainties. The new autonomous controller has a simple structure and is easy to implement. It can be adjusted according to the path tracking status to improve the adaptability of the system.