Stewart平台神经网络非奇异终端滑模控制OA北大核心CSTPCD

This paper proposes a solution to the six degrees of freedom trajectory tracking problem of the Stewart plat-form using a nonsingular terminal sliding mode control method based on a neural network.This method is applied to the position and pose control of the Stewart platform.First,a kinematic equation is established by analyzing the position in-verse solution and velocity inverse solution of the Stewart platform.Simultaneously,the dynamic equation is estab-lished based on the Newton-Euler equation.By integrating the acceleration inverse solution,we obtain the state-space representation of the platform.Subsequently,a nonsingular terminal sliding mode control law is designed using the nonsingular sliding surface function.Considering the approximation characteristics of the radial basis function(RBF)neural network,we employ this network to adaptively approximate the unknown term of the equation.An adaptive law is then designed based on the second method of Lyapunov.Finally,the effectiveness of the controller design is proved through simulations.The simulation results show that the proposed controller that uses an RBF neural network and nonsingular terminal sliding mode outperforms the proportional integral derivative controller in terms of trajectory tracking accuracy and dynamic characteristics.