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.
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.
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