Motion modeling and control of a 2-DOF soft manipulator based on a recurrent neural network
To address the difficulty of modeling and control of existing soft manipulators due to their small material stiffness and unstable modulus,this study proposes a method based on a recurrent neural network(RNN)for the motion modeling of a two-degree-of-freedom(2-DOF)soft manipulator with control.A motion-capture instrument was used to collect the position coordinates under different pressures and loads,and the coordinates were imported into a gated recurrent unit(GRU)neural network model for training.The accuracy of the test set reached 98.87％when the hyperparameters were adjusted to the optimal network structure.Accordingly,a mapping function for the pressure and load at the end position was constructed.Experimental results showed that the proposed method could improve the control accuracy of the manipulator by approximately 6～8 mm and significantly reduced the difficulty of control and modeling of a soft robot.
recurrent neural network(RNN)gated recurrent unit(GRU)modelsoft manipulatormodeling and control
万方数据
维普
