Adaptive neural control for mobile manipulator systems based on adaptive state observer

This study processes the adaptive robust control problem in a task space for a mobile manipulator with uncertain dynamics and external disturbances based on radial basis function neural networks(RBFNN) and a nonlinear state observer. Owing to the high nonlinearity, strong coupling, and unknown uncertainty characteristics of the mobile manipulators, their control poses a considerable challenge. An adaptive robust control strategy for nonlinear mobile robot systems with unknown uncertainties and disturbances based on the RBFNN and state observer is proposed in the operating space. First, a feedforward-feedback virtual speed generator is developed based on the feedforward control and backstepping method to implement virtual speed tracking control in the operation space to make the positional error asymptotically stable. Then, a model-independent RBFNN based adaptive robust controller (ARBFNNC) with the state observer is proposed, which converts the virtual speed input into the control torque of the actual mobile manipulator to achieve precise position tracking in the task space, and theoretical analysis performed through Lyapunov stability theory shows the global asymptotic stability of a system under the control of the proposed method. Finally, simulation results confirm the effectiveness of the proposed control method in position regulation and tracking control of the nonlinear mobile manipulator. © 2021 Elsevier B.V.