Research On Model Predictive Control Methods Of Human Occupied Vehicle

As land resources are increasingly depleted,Oceans reserving abundant resources with geographical significance have been paid much attention by many countries,so marine competition is becoming fiercer and fiercer.To turn China into a maritime power in this century has been added into the national strategy.And it has been taken seriously by the state that we take the deep-sea human occupied vchicles(HOV)as an effective tool for marine explorations and developments.However,the submarine environment is complex.Particularly,the resource-intensive areas such as hydrothermal source,submarine volcano and submarine canyon regions have brought a huge challenge to the safe navigation and operation of the HOV.As the core of sail control,the sail control algorithm plays a vital role in submersible sail.Supported by the National 863 Project "Control System Design and Research on the Key Technologies for the 4500 Meters HOV"(2009AA093302),this thesis designs an automatic navigation control system for the manned submersible,especially focusing on the design and verification of control structure and algorithm for automatic orientation control,automatic depth control,automatic height control and automatic dyanmic positioning functions.In this thesis,firstly,the hydrodynamic model of the HOV is studied by analyzing the forces acting on the submersible during the course of motion,especially the inertia hydrodynamic and viscous hydrodynamic forces.On the foundation of force analyses,the nonlinear mathematical model for verification and the time variant state space equation used by the algorithm are established.Secondly,according to the layout of thrusters installed in the HOV,distribution equation of force and torque is derived.The control structures are designed and control processes are formulated.In addition,the controller applying model predictive control algorithm(MPC)is designed,including initialization and main control loop structures.And constraints of the control variables are added;the method for optimal solutions of quadratic programming problems with inequality constrains is found.And state observer is designed which makes the error between estimates of the states and real states converge to zero.Finally,the effect of controller parameters on control results is studied via large numbers of experiments.Controller parameters are determined.And by comparing the simulation results of automatic orientation,depth/height control and results of fuzzy PID method,the effectiveness of the MPC algorithm is illustrated.The simulation experiments of the dynamic positioning control that is the most difficult are conducted under different environments and disturbances.And the control results are compared with the results of adaptive linear quadratic regular(LQR)method,which highlights the superiority of the model predictive control method.