Search Results (2)

With the advancement of precision agriculture, improving the operational accuracy of agricultural machinery has received increasing attention. The rice transplanter is crucial in this context, as its performance directly affects rice yield. During operation, both the magnitude and stability of the driving wheel slip ratio affect the accuracy of plant spacing, thereby influencing rice yield. However, to date, no control method that can simultaneously stabilize the speed, reduce the slip ratio, and improve the stability of the slip ratio has been proposed for transplanters. To address this issue, this paper proposes a joint speed–slip ratio control method based on an adaptive Student t-kernel maximum correntropy Kalman filter (ASMCKF) and sliding mode control (SMC). First, a Student t-kernel maximum correntropy Kalman filter (SMCKF) is designed to identify the transplanter’s speed, wheel speed, traction force, and rolling resistance in real time, thereby enhancing control system robustness against non-Gaussian heavy-tailed noise in paddy fields. An adaptive kernel bandwidth adjustment method is also introduced for the SMCKF to increase the sensitivity of the cost function to variations in the system state, thereby further improving parameter identification accuracy. Building on this, a joint speed–slip ratio control method is designed based on SMC. Simulation results confirm that the ASMCKF achieves higher identification accuracy than conventional methods when facing non-Gaussian heavy-tailed noise. Field experiment results show that the proposed method can effectively stabilize the transplanter’s speed while significantly reducing the slip ratio and improving the stability of the slip ratio. Full article

The state estimation problem is ubiquitous in many fields, and the common state estimation method is the Kalman filter. However, the Kalman filter is based on the mean square error criterion, which can only capture the second-order statistics of the noise and is sensitive to large outliers. In many areas of engineering, the noise may be non-Gaussian and outliers may arise naturally. Therefore, the performance of the Kalman filter may deteriorate significantly in non-Gaussian noise environments. To improve the accuracy of the state estimation in this case, a novel filter named Student’s t kernel-based maximum correntropy Kalman filter is proposed in this paper. In addition, considering that the fixed-point iteration method is used to solve the optimal estimated state in the filtering algorithm, the convergence of the algorithm is also analyzed. Finally, comparative simulations are conducted and the results demonstrate that with the proper parameters of the kernel function, the proposed filter outperforms the other conventional filters, such as the Kalman filter, Huber-based filter, and maximum correntropy Kalman filter. Full article