Adaptive dynamic surface control of stabilized platform in rotary steerable drilling system
A variety of interference factors underground increase the complexity of the controller design of the stabilized plat-form of the rotary steerable drilling system.In order to deal with the adverse effects of unknown friction torque and modeling error on the stabilized platform,an adaptive neural network dynamic surface control method was proposed.The RBF neural network was used to approximate the friction and disturbance torque,and the state observer was set to obtain the modeling er-ror caused by uncertainty of the relevant parameters.The dynamic surface method was introduced to avoid the"differential explosion"caused by the traditional backstepping control.Finally,the Lyapunov method was used to prove the stability of the system.The results show that the controller can make the toolface angle track the input command signal accurately and quickly under the condition of friction torque,unknown interference and modeling error in the stabilized platform model,and has good adaptability and robustness.
万方数据
维普
