A Study on the Design of Error-Based Adaptive Robust RBF Neural Network Back-Stepping Controller for 2-DOF Snake Robot’s Head

In this paper, we will propose the controller of a 2-DOF head in snake robot for effective image data reading when is driving, and present an error-based adaptive robust radial base function neural network back-stepping controller. The system nonlinear system, there are unmeasurable disturbances that occur while driving. To solve problem, use basis to compensate improve steady state. order further approximation error occurs during training or large signal changes, coefficients functions approximate design terms. Compared previous controller, proposed can actively changes has advantage not generating residual input based on Lyapunov candidate law prove stability. stability control proven through analysis bounded. compared verified performance two inputs simulation presented efficiency