Sliding Mode Control Using RBF Neural Network for Spacecraft Attitude Tracking
In order to avoid inherent chattering of sliding mode control, a radial basis function neural network based sliding mode control is presented. By using four reaction wheels and Modified Rodrigues Parameters for attitude tracking represen tation, the tracking dynamic has been considered, and inertia ma trix uncertainty, actuators uncertainty and external disturbances has been considered in the model. Divide the controller into two parts, one is the traditional sliding mode control, and the other part is neural network to estimating the systems uncertainties. The Lyapunov stability theory has been used to achieve a stable closed loop system. Simulation results illustrate the performance of the proposed algorithm. The controller successfully deals with unknown misalignments of the axis directions of the actuators, inertia matrix uncertainty and external disturbance torques.
Shiming Chen Yunfeng Dong Jianmin Su
School of Astronautics Beijing University of Aeronautics and Astronautics Beijing, P.R.China
国际会议
厦门
英文
211-214
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）