Joint Drift-Free Scheme Aided With Allowed Nonconvex Noise-Resistant Neural Networks for Repetitive Motion of Omnidirectional Mobile Manipulator
Shijun Tang, Zhongbo Sun, Yunfeng Hu, Xun Gong, Chao Cheng, Long Mei Jin · IEEE Transactions on Industrial Informatics · 2025
The joint drift problem may result in the omnidirectional mobile manipulator (OMM) failing to perform its tasks or even causing damage in practical applications. However, the coefficients for eliminating joint drift are coupled with the equation constraints in Cartesian space under the existing scheme, which theoretically results in a paradox between zero joint drift and zero positional error in Cartesian space. To address the joint drift, a joint drift-free repetitive motion programme with position error feedback (JDF-RMPPEF) is presented and analyzed. The JDF-RMPPEF scheme decouples the joint error and position error, which enables the OMM to accurately perform the trajectory tracking and repetitive motion tasks. In addition, to suppress the disturbances and solve the JDF-RMPPEF problem accurately, an allowed nonconvex noise-resistant neural network (ANNRNN) model is proposed, which allows for a nonconvex activation function with noise suppression properties. Theoretical analysis demonstrates that the ANNRNN model exhibits global convergence and strong robustness in the presence of interference. Through examples and comparisons, the effectiveness and superiority of the JDF-RMPPEF scheme synthesized by the ANNRNN model are validated.