Research on the inverse kinematics prediction of a soft biomimetic actuator via BP neural network

Inspired by the pneumatic artificial muscle, soft biomimetic pneumatic actuators have been applied in many applications due to their high flexibility, good environmental adaptability, and safe interaction with the surroundings. In this work, we address the inverse kinetics problem of motion planning of soft biomimetic actuators driven by three chambers. Although the mathematical model describing the inverse dynamics of this kind of actuator has been employed, this model is still a complex system. On the one hand, the differential equations are nonlinear. Therefore, it is complicated and time-consuming to get analytical solutions. Since the exact solutions of the mechanical model are not available, the elements of the Jacobian matrix cannot be calculated exactly. On the other hand, the material model is a complicated system with significant nonlinearity, non-stationarity, and uncertainty, making it challenging to develop an appropriate system model. To overcome these intrinsic problems, we propose a back-propagation (BP) neural network learning the inverse kinetics of the soft biomimetic actuator moving in three-dimensional space. After training with sample data, the BP neural network model can represent the relation between the manipulator tip position and the pressure applied to the chambers. The proposed algorithm is very precise and computationally efficient. The results show that a desired terminal position can be achieved with a degree of accuracy of 2.46% relative average error with respect to the total actuator length, demonstrating the ability of the model to realize the inverse kinematic control of the soft biomimetic actuator.