Adaptive Event-triggered Control For Unmanned Surface Vehicles Subject To Deception Attack Environments

The unmanned surface vehicle has become one of the hot research issues owing to its industrial advantages of intelligence and the efficient in the marine transportation.In recent years,with the thorough research on the intelligent navigation control theory of unmanned surface vehicle(USV),the intelligent control system of unmanned surface vehicle has been also constantly applied in a variety of practical and complex scenarios,such as marine environment monitoring,maritime search and rescue operations,military combat patrol and marine ranching.However,with the rapid development of unmanned surface vehicle intelligent control theory system,there are still some challenges in the research of unmanned surface vehicle intelligent control theory system due to the unmanned surface vehicle control system has the network interconnection characteristics of the general cyber-physical system.For example,the cyber-attack maliciously changes the system signal,system communication resources are limited,external marine environment disturbances and uncertain nonlinear model.These problems may reduce the path-following robustness of unmanned surface vehicles and even lead to the instability of the whole system.Aiming at the challenges faced by the path-following control system of unmanned surface vehicles in autonomous navigation tasks,this thesis designs a robust adaptive control algorithm.The semi-global uniform finally bounded(SGUUB)stability of the USV control system is further verified by the Lyapunov stability theory.In addition,the simulation experiment of the control algorithm has been carried out in the simulated marine environment,which effectively verifies the anti-cyber-attack performance and robustness of the proposed algorithm.The main contributions of this thesis can be summarized as follows:(1)An adaptive event-triggered path-following control algorithm based on double layer virtual ship(DLVS)guidance is proposed for underactuated unmanned surface vehicles with unknown actuator gain,time-varying disturbances of external marine environment and network cyber deception attacks.In the DLVS guidance unit,adaptive virtual ship(AVS)is developed and designed to obtain smooth reference paths of unmanned surface vehicles.The introduction of AVS can effectively alleviate the chattering phenomenon of actuator signals.Under the DLVS guidance strategy framework,the robust adaptive controller of the unmanned surface vehicle is designed by utilizing an idea of integrating the event-triggered mechanism(ETM)and adaptive backstepping compensation techniques.In the kinematics controller design part,by constructing adaptive parameters of deception attack,the unmanned surface vehicle can obtain good control performance in presence of deception attacks.It is very important to improve the control accuracy and stability of the unmanned surface vehicle path-following closed-loop system.In addition,the advantage of input-based event-triggered mechanism relieves the communication channel burden of the transmission of control information from the controller to the actuator.Finally,the theoretical stability of the proposed control algorithm has been proved by employing Lyapunov theorem,and the numerical simulation experiment is carried out on the MATLAB simulation platform to obtain better control performance.(2)Aiming at the problems of the uncertainty model structure,the unknown control direction gain and limited communication resources in the path-following control task of unmanned surface vehicles under the cyber deception attack environment,In the guidance virtual ship-dynamic virtual ship(GVS-DVS)guidance framework,the event-triggered path-following control algorithm based on Nussbaum-type function is proposed to copy with the influence of deception attacks.This algorithm employs radial basis function neural networks(RBF-NNs)to effectively approximate the nonlinear structure of the unmanned surface vehicle model.At the same time,under the double excitation of robust neural damping and dynamic surface control(DSC)techniques,the frequent learning and updating of the weight of the neural network is avoided.Moreover,the computational load of the control algorithm is saved.The Nussbaum-type gain function is designed to deal with the problem of unknown control direction gain in the actuator due to deception attacks.In addition,an input-based event-triggered mechanism is designed to solve the problem of channel blockage and resource waste between the controller and the actuator.The analysis of Lyapunov stability theory shows that the correlated error variables of the unmanned surface vehicle closed-loop path-following system are semi-global uniformly ultimately bounded(SGUUB).Through computer simulation experiments under the simulated marine environment interference,it is proved that the proposed algorithm has good anti-cyber-attack control performance and robustness.This thesis mainly studies the path-following task of unmanned surface vehicles from two aspects of guidance and control.In the aspect of guidance,the guidance law of the unmanned surface vehicle is designed based on the navigation situation of waypoints.In terms of control,the problems of deception attack maliciously changing system signals and limited system communication resources faced by unmanned surface vehicles in the course of path-following are solved.It is of great significance to accelerate the application of unmanned surface vehicle in engineering practice.