Study Of Adaptive Hybrid Intelligent Optimization Algorithms And Their Applications In Marine Dynamic Positioning System

Thrust force is produced by thruster which installed in the dynamic positioning ship to against the external interference,keeping the DP ship or offshore platform in a desired position of the ocean.Because of the advantage of great working performance without the operating depth,convenient operation and accurate positioning,the dynamic positioning technology has becomed one of key technologies in the ocean oil exploration and development recent years.In dynamic positioning system,the force and moment ship needed to against the deviation of position are calculated by high-level controller based on the input deviation and then they are transformed into thrust and angle signal of each thruster via thrust allocation system.Finally,the propulsion motor drives the propeller under the control of low-level controller.The controller performance is great influenced by its control parameter and a satisfying performance could be got with suitable parameter.In addition,the optimization of thrust allocation could not only reduce the error,but also save energy and decrease mechanical wear.In this thesis,mathematical model is set up to study the control parameter optimization of high-level controller and low-level controller and the thrust allocation problem of one ship which equipped with dynamic positioning system.Along with the development of the algorithm,a group of novel intelligent algorithms are used to solve the above problems.In order to solve the control parameter optimization problem of high-level controller,one high-level controller is designed firstly based on the active disturbance rejection control(ADRC)technology and the movement of one actual ship.Three ADRC controllers are used to control the surge,sway and yawing motion of the ship respectively.And the performance of high-level controller is studied under the condition of ideal state of ocean and four level of ocean respectively.Then,an adaptive DE-BBO hybrid intelligent optimization algorithm is designed based on the differential evolution(DE)algorithm and biogeography-based optimization(BBO).In this algorithm,one individual could choose search model for next iteration adaptively based on the performances of each model in the previous itration.Finally,the parameters of high-level controller are optimized via adaptive DE-BBO algorithm and thus the control performance and robustness of controller are improved.In order to deal with the thrust allocation problem,an adaptive DE-PSO hybrid intelligent optimization algorithm is provided based on the differential evolution(DE)algorithm and particle swarm optimization(PSO).Compared with DE algorithm and PSO algorithm,adaptive DE-PSO algorithm could reduce the thrust error,speed up the convergence and decrease the energy consumption when solve the thrust allocation problem.In order to deal with the control parameter optimization problem of low-level controller,one low-level controller is designed firstly based on fuzzy vector control technology and permanent magnet synchronous motor.Then,an adaptive ABC-PSO hybrid intelligent optimization algorithm is provided based on the artificial bee colony(ABC)algorithm and particle swarm optimization(PSO)and used to optimize the control parameters of low-level controller to improve its control performance.Finally,the whole content of this thesis is summarized and future work is also pointed out.