Study On Intelligent Control And Guidance Law Of Supercavitating Vehicle At Variable Cavitation Number

Supercavitation technology can fundamentally change the resistance characteristic of underwater vehicle which can achieve an underwater drag-reducing effect up to 90%.Thus,the supercavitation technology can greatly promote the combat effectiveness of underwater weapons,which has been of great value in military applications.Meanwhile,there exist distinct differences between the supercavitating vehicle and traditional underwater vehicle.These differences are mainly included in the configuration and the hydrodynamic characteristics.Especially,when the vehicle is at the acceleration stage,the cavity will becomes longer until covering the whole surface of the vehicle,so the hydrodynamic characteristic will be more complicated.Above differences will pose a great challenge for dynamic modeling,control and guidance of supercavitating vehicle.Based on the research of supercavitation flow and hydrodynamic characteristic,the dynamic model of variable cavitation number supercavitating vehicle is established,and then the nonlinear control and guidance problem is studied deeply by means of intelligent algorithm.The main topics and results of this thesis are as follows:The nonlinear dynamic model is established,when the evolution of cavitation flow field and hydrodynamic characteristic of supercavitating vehicle are investigated at the acceleration and cruise stage.Firstly,considering the influences of cavity memory effect,gravity and cavitator orientation effect on the deviation of cavity axis,the shape of unsteady supercavity is predicted based on the principle of cavity expansion independence.On this basis,the hydrodynamic forces on each region are analyzed in detail according to the wet degree.Combining the calculation model of tail thrust which is proposed based on the Rayleigh-Plesset equation,and the nonlinear dynamic model of variable cavitation number supercavitating vehicle is eventually established.And then the trajectory and stability rules are given under the uncontrolled case through the trajectory simulation of supercavitating vehicle.In order to realize the attitude maneuver control of supercavitating vehicles without uncertainties,an optimal controller is designed according to the optimal control theory.In this algorithm,the dynamic model is decoupled and linearized by the precision linearization method.The simulation results show that the precise linearization method can effectively solve the problem of the attitude maneuver control for the supercavitating vehicle under the condition of ignoring uncertainties.Based on the sliding mode variable structure control theory and the fuzzy control theory,three robust controllers are designed respectively for the attitude maneuver control of the supercavitating vehicle under uncertainties.The former uses the backstepping approach the design the sliding mode controller,and uses the nonlinear damping technique to remove the disturbance of the system uncertainties.The latter designs a fuzzy sliding mode controller which takes the sliding mode function and its derivative as the input of fuzzy controller.Meanwhile,the performance and characteristic of the two controllers are analyzed by simulation.Considering the shortcomings of the two controllers,a new reaching law is proposed which not only can increase the reaching speed but also decrease the chattering apparently,and then an indirect adaptive fuzzy sliding mode controller is designed.The fuzzy-logic system is used to approximate the unknown nonlinear function,at the same time the approximation error and the external disturbance are compensated by the adaptive algorithm.Simulation results show that the control system has good robust stability and anti-interference ability,and it is also avoid the planing force effectively under the conditions of parameter perturbation and external disturbance.The bubbles number density of ship wakes is established by a mathematical model.For the supercavitating vehicle taking the large surface ships as a target,an adaptive RBF neural network sliding mode guidance law is designed based on the gradient of bubbles number density.In this algorithm,the switching function is considered as the input of the RBF neural network and the connection weights can be adjusted in real time by the adaptive algorithm.The simulation results show that the supercavitating vehicle can quickly track the trajectory of the wake,and the miss distance is small and the voyage is reduced significantly.The problem of the guidance law design for the supercavitating anti-torpedo is investigated.Firstly a sliding mode guidance law is designed based on zeroing line of sight rate.Then the RBF neural network is employed to compensate the target maneuver,which overcomes the unknown upper bound problem.And according to the distance between the system and the sliding surface,the gain of the sliding mode guidance law is adjusted adaptively to reduce the chattering.The simulation results show that the guidance law has strong robust performance and small miss distance,also the trajectory is smooth and the normal overlord is low in the case of target maneuver.