Designing a controller to stabilize maneuvering hovercrafts is an important challenge in amphibious vehicles.Hovercrafts are implemented in several applications,such as military missions,transportation,and scientific ...Designing a controller to stabilize maneuvering hovercrafts is an important challenge in amphibious vehicles.Hovercrafts are implemented in several applications,such as military missions,transportation,and scientific tasks.Thus.to improve their performance,it is crucial to control the system and compensate uncertainties and disruptions.In this paper,both classic and intelligent approaches are combined to design an observer-based controller.The system is assumed to be both controllable and observable.An adaptive neural network observer with guaranteed stability is derived for the nonlinear dynamics of a hovercraft,which is controlled via a nonsingular super-twisting terminal sliding-mode method.The main merits of the proposed method are as follows:(1) the Lyapunov stability of the overall closed-loop system,(2) the convergence of the tracking and observer errors to zero,(3) the robustness against uncertainties and disturbances,and(4) the reduction of the chattering phenomena.The simulation results validate the excellent performance of the derived method.展开更多
文摘Designing a controller to stabilize maneuvering hovercrafts is an important challenge in amphibious vehicles.Hovercrafts are implemented in several applications,such as military missions,transportation,and scientific tasks.Thus.to improve their performance,it is crucial to control the system and compensate uncertainties and disruptions.In this paper,both classic and intelligent approaches are combined to design an observer-based controller.The system is assumed to be both controllable and observable.An adaptive neural network observer with guaranteed stability is derived for the nonlinear dynamics of a hovercraft,which is controlled via a nonsingular super-twisting terminal sliding-mode method.The main merits of the proposed method are as follows:(1) the Lyapunov stability of the overall closed-loop system,(2) the convergence of the tracking and observer errors to zero,(3) the robustness against uncertainties and disturbances,and(4) the reduction of the chattering phenomena.The simulation results validate the excellent performance of the derived method.
