Chen Naijian

Coordination control strategy between human vision and wheelchair manipulator based on BCI

To assist activities of daily living, a wheelchair and its mounted manipulator is developed using a coordination control strategy in this paper. The coordination control strategy is composed of a operating intention express and identifying with EEG, locating objective based on EOG and head gesture and a human-robot interface. EEG is used to be acquired and identified the operating intention through users motion imagery such as left and right legs, left and right hands and so on to express control instructions for the wheelchair and its mounted manipulator. EOG signals and user head gesture are used to locate the operating objective. The EEG and EOG identification systems are integrated in the human-robot interface effectively. Experimental results show that the coordination control strategy between human and wheelchair manipulator based on BCI is effectiveness and validity in assisting activities of daily living.

Adaptive robust control of the mobile manipulator based on neural network

This article describes an adaptive robust control algorithm of the mobile manipulator based on robust neural network to solve disturbances, nonlinearity, nonholonomic constraints between the mobile platform and the mounted manipulator. According to the nonlinear mapping ability of neural network and fast learning ability, neural network and robust control strategies are integrated into the adaptive control algorithm for the mobile manipulator with nonlinear and nonholonomic constraints. Basis on the mobile manipulator dynamics model and its modeling analysis, the controller can drive the trajectory tracking error of the mobile manipulator to converge to zero. The simulation results show that the algorithm has higher precision of trajectory tracking control and can achieve good control effect.