We studied carrier landing robust control based on longitudinal decoupling.Firstly,due to the relative strong coupling between the tangential and the normal directions,the height and the velocity channels were decoupl...We studied carrier landing robust control based on longitudinal decoupling.Firstly,due to the relative strong coupling between the tangential and the normal directions,the height and the velocity channels were decoupled by using the exact linearization method,so that controllers for the two channels could be designed seperately.In the height control,recursive dynamic surface was used to accelerate the convergence of the height control and eliminate″the explosion of complexity″.The radial basis function(RBF)neural network was designed by using the minimum learning parameter method to compensate the uncertainty.A kind of surface with nonsingular fast terminal sliding mode and its reaching law were developed to ensure finite time convergence and to avoid singularity.The controller for the velocity was designed by using super-twisting second-order sliding mode control.The stability of the proposed system was validated by Lyapunov method.The results showed that the Levant′s robust differential observer was improved and used for the observation of the required higher order differential of signals in the controller.The response of aircraft carrier landing under the complex disturbance is simulated and the results verified the approach.展开更多
基金supported in part by the National Natural Science Foundation of China(No.51505491)
文摘We studied carrier landing robust control based on longitudinal decoupling.Firstly,due to the relative strong coupling between the tangential and the normal directions,the height and the velocity channels were decoupled by using the exact linearization method,so that controllers for the two channels could be designed seperately.In the height control,recursive dynamic surface was used to accelerate the convergence of the height control and eliminate″the explosion of complexity″.The radial basis function(RBF)neural network was designed by using the minimum learning parameter method to compensate the uncertainty.A kind of surface with nonsingular fast terminal sliding mode and its reaching law were developed to ensure finite time convergence and to avoid singularity.The controller for the velocity was designed by using super-twisting second-order sliding mode control.The stability of the proposed system was validated by Lyapunov method.The results showed that the Levant′s robust differential observer was improved and used for the observation of the required higher order differential of signals in the controller.The response of aircraft carrier landing under the complex disturbance is simulated and the results verified the approach.
