Ship Dynamic Positioning Control System Design Based On Cloud Model Algorithm

The central part of ship dynamic positioning system(DPS) is its control system that has a direct effect on actual performance of DPS and application of the ship, therefore it is always the dynamic positioning control research focus to search more advanced and mature DPS control theories and methods.The uncertain marine disturbances such as wind, wave and current are widespread in ship movement course, so the dynamic positioning control process is an uncertain and nonlinear system whose uncertainty is particularly prominent especially when the marine environment changes in a wide range. Cloud model control theory is a new subject in intelligent control domain and available to control complex nonlinear systems, the basic ideology of which is to realize people’s control experience by computer. Furthermore, the cloud model qualitative reasoning method that doesn’t need accurate mathematic models of control object not only can keep various uncertainties contained in controlled plant and its environment, but also has strong adaptability and robustness for disturbances, processing capacity for nonlinearity and engineering practicality. Considering the advantages of cloud model control algorithm, the innovation of this paper is applying it to design the DPS control system and aims to solve ship dynamic positioning control problems with allowing for its characteristics of large inertia, nonlinearity, time-variety and uncertainty of models by means of studying cloud model control technique. The main research works are as follows:1) Established DPS mathematic models and introduced cloud model basic theories.2) Designed ship dynamic positioning one-dimension cloud model controller based on one-dimension cloud model mapping algorithm and one-dimension complex cloud model controller on that basis, and constructed two types of two-dimension cloud model controllers for DPS with different double conditions multi-rules cloud mapping algorithms respectively based on normal membership clouds and hybrid membership clouds.3) To avoid premature convergence and slow convergence velocity in later search period of particle swarm optimization algorithm(PSO), improved it in particle swarm initialization, diversity preservation, control parameters regulation and boundary strategies adjustment with chaos algorithm, genetic algorithm, one-dimension cloud model algorithm and two-dimension cloud model algorithm severally.4) As setting the DPS cloud model controller characteristic parameters with traditional design method is time-consuming, complicated and difficult to get ideal results, proposed cloud model controller parameters auto-tuning principles based on standard particle swarm optimization and its improved algorithms correspondingly.5) Since the performance of DPS under the control of nothing but PID is not very good, designed an intelligent PID controller which can adjust its control parameters actively in real time on the basis of combining superiorities of chaos particle swarm optimization, two-dimension cloud model control and traditional PID control.This thesis enriched the DPS control methods and can be used as a certain degree of guidance and reference for DPS controllers design in practical engineering.