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 (μ) 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.
Highlights
In precision agriculture, the primary objectives are to improve production efficiency; reduce environmental degradation; and, more importantly, optimize the use of available resources such as energy, water, seeds, and fertilizer
It is an improved form of model following control (MFC), and it centers on comparing the maximum torque developed based on the vehicle dynamic model and the one measured using sensors
The study proposed and developed an efficient and feasible means of estimating an optimum slip that corresponds to maximum traction and low soil disturbances
Summary
The primary objectives are to improve production efficiency; reduce environmental degradation; and, more importantly, optimize the use of available resources such as energy, water, seeds, and fertilizer. MTTE is another means of traction control without the need to estimate vehicle speed It is an improved form of MFC, and it centers on comparing the maximum torque developed based on the vehicle dynamic model and the one measured using sensors. Traction control that is more robust to changes in terrain types, the unknown wheelterrain conditions, and does not have the limitations of MFC and MTTE was achieved through slip feedback control It is only applicable where a reliable estimate of vehicle velocity is available. It was found that the optimum value of slip is dependent on tires, operation parameters, and road conditions These have led to the development of slip controls based on a real-time estimate of the optimum slip, which correspond to high friction between tire and road surfaces. The system was implemented on an electric vehicle test platform and tested in different terrain types
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