Neural Network-Based Cooperative Trajectory Tracking Control for a Mobile Dual Flexible Manipulator

For a mobile dual flexible manipulator (MDFM) system, this article focuses on the problem of cooperative trajectory tracking under unknown dynamics and time-varying trajectories. The dynamic model of the wheeled mobile manipulator system in 2-D space is established. Taking into account the unmodeled dynamics of the system, unknown terms of the system are approximated by integrating the radial basis function neural network (RBFNN) structure. By introducing the servo system, the cooperative trajectory tracking control (CTTC) strategy is designed, which realizes the system's cooperative operation, time-varying trajectory tracking, and vibration suppression. The performance of the proposed control scheme is verified through theoretical analysis and numerical simulations.