Adaptive Fuzzy Sliding Mode Control Based on Genetic Optimization for Rotary Steering Drilling Stabilized Platform

Abstract

Considering the robust control of stabilized platform of rotary steering drilling system, an adaptive fuzzy sliding mode control strategy based on genetic optimization is presented. Firstly, the universal approximation property of fuzzy system is used to approximate the uncertain external disturbance upper bound of stabilized platform under wording condition. Subsequently, sliding mode controller is designed to guarantee the robustness of the closed-loop system and sign function is replaced by bipolar sigmoid function to weaken chattering. Finally, genetic algorithm (GA) is applied to search the optimal controller parameters, including switching function coefficient, membership function of fuzzy system, adaptive coefficient of fuzzy system and sigmoid function coefficient. Simulation results show that this control strategy can make stabilized platform achieve optimal control performance and robustness.