Development of Online Adaptive Traction Control for Electric Robotic Tractors

Idris Idris Sunusi, Jun Zhou, Chenyang Sun, Zhenzhen Wang, Jianlei Zhao and Yongshuan Wu
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Idris Idris Sunusi: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Jun Zhou: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Chenyang Sun: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Zhenzhen Wang: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Jianlei Zhao: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Yongshuan Wu: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China

Energies, 2021, vol. 14, issue 12, 1-24

Abstract: Estimation and control of wheel slip is a critical consideration in preventing loss of traction, minimizing power consumptions, and reducing soil disturbance. An approach to wheel slip estimation and control, which is robust to sensor noises and modeling imperfection, has been investigated in this study. The proposed method uses a simplified form of wheels longitudinal dynamic and the measurement of wheel and vehicle speeds to estimate and control the optimum slip. The longitudinal wheel forces were estimated using a robust sliding mode observer. A straightforward and simple interpolation method, which involves the use of Burckhardt tire model, instantaneous values of wheel slip, and the estimate of longitudinal force, was used to determine the optimum slip ratio that guarantees maximum friction coefficient between the wheel and the road surface. An integral sliding mode control strategy was also developed to force the wheel slip to track the desired optimum value. The algorithm was tested in Matlab/Simulink environment and later implemented on an autonomous electric vehicle test platform developed by the Nanjing agricultural university. Results from simulation and field tests on surfaces with different friction coefficients (?) have proved that the algorithm can detect an abrupt change in terrain friction coefficient; it can also estimate and track the optimum slip. More so, the result has shown that the algorithm is robust to bounded variations on the weight on the wheels and rolling resistance. During simulation and field test, the system reduced the slip from non-optimal values of about 0.8 to optimal values of less than 0.2. The algorithm achieved a reduction in slip ratio by reducing the torque delivery to the wheel, which invariably leads to a reduction in wheel velocity.

Keywords: electric tractors (ET); sliding mode control; wheel slip control; driving force observer; Burckhardt traction model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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