An Intelligent Sliding Mode Control Scheme for Stabilized Platform of Rotary Steering Drilling Tool

Abstract

Considering the nonlinear and uncertain influencing factors on stabilized platform of rotary steering drilling tool under work condition, a new intelligent sliding mode control strategy is presented for stabilized platform. The system robustness is ensured by sliding mode control. The upper bound of overall uncertainty is nonlinear approximated by RBF neural network, which can make the uncertain upper bound adjust adaptively. The chattering is reduced by quasi-sliding mode method. Finally, particle swarm optimization algorithm is applied to search the optimal controller parameters, including boundary layer thickness, switching function coefficient and adaptive parameter of neural network weight, Simulation results show that this control scheme can make stabilized platform get a good control performance and robustness.