Robust Path Following of the Tractor-Trailers System in GPS-Denied Environments

Trajectory tracking is one of the key technologies for high-precision agriculture and has been the research focus by a large number of academics. In order to design a simple trajectory tracking control scheme, an adaptive sliding mode control based on lateral deviation is proposed. To reduce the chattering phenomenon, an adjustable parameter associated with the sliding surface and system error is introduced, by which the forward gain value can be regulated with the state change. According to the established tractor model, the desired front wheel angle can be obtained as the output of the sliding mode controller. A modified Smith predictor structure based two degree of freedom control scheme is designed to follow the desired front wheel angle. The effectiveness of the designed trajectory tracking control system is demonstrated by simulations, especially the performance of preventing chattering phenomenon.

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“…Where 0 0 01 . When the state is near the sliding surface,ˆ/ ( 1) ee . will tend to be zero with e decreases.…”

Section: B Adaptive Sliding Controller Designmentioning

confidence: 99%

“…It is tedious for driver to operate a long time. Automatic guidance is accurate automatic control of tractor or implement along a predefined trajectory [1]. Agricultural machinery equipped with automatic navigation system can reduce the labor intensity of a driver and bring ecological advantages.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Based on Adaptive Sliding Mode Control for Agricultural Tractor

Yin

Wang

et al. 2020

IEEE Access

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“…A tracking controller was designed in [20] by considering the friction influence. A minimum swept path control design was discussed for TTWMRs in [21] and a robust path following control design was studied in [22]. From a deep literature review, the platoon formation control of TTWMRs has not been paid attention in the literature at all and it is the main topic of this study.…”

Section: Introductionmentioning

confidence: 99%

Observer‐based neural adaptive control of a platoon of autonomous tractor–trailer vehicles with uncertain dynamics

Elhaki

Shojaei

2020

IET Control Theory & Appl

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“…The intelligent navigation method such as the Global Navigation Satellite System (GNSS) [2], [3] was introduced to tractor autonomous driving in the field but will fail intermittently because of the satellite signal blocked by the trees occasionally.…”

Section: Introductionmentioning

confidence: 99%

Tractor Assistant Driving Control Method Based on EEG Combined With RNN-TL Deep Learning Algorithm

et al. 2020

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Nowadays, fieldwork is often accompanied by tight schedules, which tends to strain the shoulder muscles due to high-intensity work. Moreover, it is difficult and stressful for the disabled to drive agricultural machinery. Besides, current artificial intelligence technology could not fully realize tractor autonomous driving because of a high uncertain filed environment and short interruptions of satellite navigation signal shaded by trees. To reduce manual operations, a tractor assistant driving control method was proposed based on the human-machine interface utilizing the electroencephalographic (EEG) signal. First, the EEG signals of the tractor drivers were collected by a low-cost brain-computer interface (BCI), followed by denoising using a wavelet packet. Then the spectral features of EEG signals were calculated and extracted as the input of Recurrent Neural Network (RNN). Finally, the EEG-aided RNN driving model was trained for tractor driving robot control such as straight going, brake, left turn, and right turn operations, which control accuracy was 94.5% and time cost was 0.61 ms. Also, 8 electrodes were selected by the PCA algorithm for the design of a portable EEG controller. And the control accuracy reached 93.1% with the time cost of 0.48 ms. To solve the incomplete driving data set in the actual world because some driving manners may cause dangerous or even death, RNN-TL algorithm was employed by creating the complete driving data in the virtual environment followed by transferring the driving control experience to the actual world with small actual driving data set in the field, which control accuracy was 93.5% and time consumption was 0.48 ms. The experimental results showed the feasibility of the proposed tractor driving control method based on EEG signal combined with RNN-TL deep learning algorithm which can work with the displacement error less than 6.7 mm when the tractor speed is less than 50 km/h.

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Robust Path Following of the Tractor-Trailers System in GPS-Denied Environments

Cited by 28 publications

References 23 publications

Trajectory Tracking Based on Adaptive Sliding Mode Control for Agricultural Tractor

Trajectory Tracking Based on Adaptive Sliding Mode Control for Agricultural Tractor

Observer‐based neural adaptive control of a platoon of autonomous tractor–trailer vehicles with uncertain dynamics

Tractor Assistant Driving Control Method Based on EEG Combined With RNN-TL Deep Learning Algorithm

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