Design of Control System for Autonomous Harvester Based on Navigation Inputs

Chatterjee, A.; Peeyush, K. P.

doi:10.1007/978-981-15-5558-9_96

Cited by 1 publication

(1 citation statement)

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“…Path tracking controllers are developed to ensure vehicles track a predefined path by calculating the desired control variables. In terms of vehicle models used and the control strategies used, the path tracking control approaches are manifold (Jeon et al , 2017; Dong et al , 2011; Ding et al , 2018; Chatterjee and Peeyush, 2020). Each control approach has its own advantages and disadvantages, and the appropriate controller should be designed according to the specific application requirements.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Wang

Liu³

2021

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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.

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