Adaptive neural network control for stabilizing sphere of floated inertial platform using rotation vector

Abstract

This paper presents a neural network adaptive control scheme for the sphere three-dimensional stabilization of the floated inertial platform in consideration of the shell rotation and unknown attributions. Firstly, based on the analysis of the moments acting on the sphere, the dynamic model of the rotational sphere is established in view of the unknown attributions including mass distribution, hydrodynamic drag, electric brush friction, disturbances etc. Secondly, a radial basis function neural network is designed to identify the real dynamic model. Thirdly, the rotation vector from current attitude to the reference attitude is selected as the input of the controller according to the Euler rotation theorem. An updating law of the control parameters is derived from the radial basis function neural network. The adaptive controller is designed using this rotation vector and the updating law. This method addresses the problem of different dimensions between the two state variables of the dynamic model, which is derived from the sphere attitude expressed by a quaternion. Finally, simulation examples are provided to verify the effectiveness and robustness of the designed controller.