Adaptive Gliding-Guided Projectile Attitude Tracking Controller Design Based on RBF Neuro-sliding Mode Technique

In this paper, a new hybrid scheme which combines radial basic function (RBF) neural network with a model following sliding mode control technique to take their common features is used to solve attitude control problem of gliding guide projectile. The attitude kinematics model described by second-order nonlinear uncertain system is divided into two single-input single-output subsystems by considering the nonlinearity as disturbance. To avoid generating high control value, the coupled inputs are kept as one nominal input instead of being included in lumped uncertainties. The uncertainties in the plant are cancelled by an adaptive RBF neural networks estimator, which is designed based on Lyapunov theory. To verify the effectiveness of the proposed control strategy, attitude tracking control experiments are simulated under strong internal and external disturbances.