Study on Orchard Vehicle Motion Stability Control System

Wang, Xin; Xu, Min; Li, Weiwei; Hongsong, Lu; Zhang, Shaohong; Wang, Shumao

doi:10.1016/j.ifacol.2016.10.082

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Data acquisition module uses Microprogrammed Control Unit (MCU), MC9S12XS128 as the core. The MC9S12XS128 is a 16-bit single-chip microcomputer with powerful functionality, strong stability and efficient computation capacity [21]. It is equipped with 8KB RAM and 128KB flash, sufficient in this system.…”

Section: Detection Systemmentioning

confidence: 99%

Detection of the Trench Depth Based on a Differential Inertial Sensor Technology

Wang¹,

Peng²,

Miedziński³

et al. 2018

ElAEE

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The article proposes the use of a differential inertial sensors system for detection of the trench depth under operation. The inertial sensors are employed to detect a posture of the trencher and as a result to determine an angle difference through inertial sensors located on a vehicle (as a reference) and on the trencher arm. In this way, the undesirable, disturbing effects caused by unevenness of the field etc. are significantly reduced or even eliminated. The trench depth calculation procedure combines the angle difference and relative dimensions of the trencher structure. It is displayed on a monitor in real time. To perform a remote monitoring, the system is equipped with wireless transmission module using an upper-computer. To decrease data oscillation under operation, the calculated angles need to be filtered. Therefore, simulation based on Kalman filter in MATLAB was performed and found Q/R rate takes an important role in filtering and filtering test verified it. In field test, error rate between trench depth measured and detected is within 5 % what confirms the practical usefulness of the developed measuring system for the trench depth detection.

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Section: Detection Systemmentioning

confidence: 99%

Detection of the Trench Depth Based on a Differential Inertial Sensor Technology

Wang¹,

Peng²,

Miedziński³

et al. 2018

ElAEE

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show abstract

“…2. Specifically speaking, authors use ADAMS to set up virtual prototype of scale model, conduct vehicle motion stability analysis in orchard road conditions, rely on MATLAB/Simulink module to make control algorithm co-simulation [13]- [15] based on dynamic characteristics of virtual prototype model, adjust parameters of control model to optimize control method, and finally verify practicability and availability of control algorithm by solid vehicle test of scale model, whose main parameters are shown in Fig. 1.…”

Section: A Scale Modelmentioning

confidence: 99%

Investigation of PID Control Model Performance in Off-road Working Vehicle Motion Stability Control System

Xu¹,

Wang²,

Li³

et al. 2017

ElAEE

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This paper focuses on tip-over problem of orchard vehicle, establishes vehicle dynamic model with 7 degrees of freedom (DOF), and proposes a vehicle motion stability control method based on real-time adjustment of vehicle posture parameters, relies on ADAMS and MATLAB to set up co-simulation control model, and conducts vehicle scale model motion stability control simulation and test. Analysis of simulation result shows that vehicle motion stability control model mentioned above can effectively prevent orchard vehicle tip-over accidents. To verify simulation result, vehicle state parameter collection system is established to acquire information on vehicle velocity and posture in real time. It is found in the same environment, including road conditions and parameter adjustment, vehicle scale model test and co-simulation result have a basically consistent variation tendency. When vehicle encounters tip-over accident, angles such as pitching angle vary rapidly. In anti-tip-over control process, pitching and rolling angle have greater influence on vehicle motion stability. If Kp and Kd are adjusted properly, good vehicle motion stability control effect is acquired.

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“…In recent years, machine learning and deep learning combined with traditional feature extraction methods, such as frequency-domain features [8]- [12], wavelet energy entropy, have performed well in fault diagnosis and prediction. Since the introduction of Alex Net [13] in 2012, the deep neural network has been widely used in fault diagnosis and prediction.…”

Section: Introductionmentioning

confidence: 99%

Combine Harvester Cooling Water Temperature Prediction Based on CDAE-LSTM Hybrid Model

et al. 2021

ELEKTRON ELEKTROTECH

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Cooling water temperature of the combine harvester during operations can reflect the changes of its power consumption and even overloads caused by extreme workload. There is an existing problem when extracting water temperature information from harvesters: data redundancy and the loss of time series feature. To solve such problem, a Convolutional denoising autoencoder and Long-Short Term Memory Artificial Neural Network (CDAE-LSTM) hybrid model based on parameter migration is proposed to predict temperature trends. Firstly, the historical data of the combine harvester are taken into account to perform correlation analysis to verify the input rationality of the proposed model. Secondly, pre-training has been performed to determine the model’s initial migration parameters, along with the adoption of CDAE to denoise and reconstruct the input data. Finally, after the migration, the CNN-LSTM hybrid model was trained with a real dataset and was able to predict the cooling water temperature. The accuracy of the model has been verified by field test data gathered in June 2019. Results show that the root mean squared error (RMSE) of the model is 0.0817, and the mean absolute error (MAE) is 0.0989. Compared with the performance of LSTM on the prediction data, the RMSE improvement rate is 2.272 %, and the MAE improvement rate is 20.113 %. It is proven that the adoption of CDAE stabilizes the model, and the CDAE-LSTM hybrid model shows higher accuracy and lower uncertainty for time series prediction.

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Study on Orchard Vehicle Motion Stability Control System

Cited by 4 publications

References 10 publications

Detection of the Trench Depth Based on a Differential Inertial Sensor Technology

Detection of the Trench Depth Based on a Differential Inertial Sensor Technology

Investigation of PID Control Model Performance in Off-road Working Vehicle Motion Stability Control System

Combine Harvester Cooling Water Temperature Prediction Based on CDAE-LSTM Hybrid Model

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