Application Study Of MPC On Dredging Optimization Of Trailing Suction Hopper Dredger

With the rapid development of science and technology, our dredging cause has madegreat progress and dredging equipment has also got updated. Great progress has made indredging equipment in the aspect of its large scale and automation, while the high efficiencyof dredging is still lack of study. On the basis of dredging equipments, the improvement ofdredging performance and elevating dredging efficiency are the aspects what our dredgingcause should be researched and developed. Research on the mechanism of trailing suctionhopper dredger dredging and search of the optimal ways will mean to the improvement ofthe dredger’s efficiency of construction and the market competitiveness.On the foot of the mechanism of trailing suction hopper dredger dredging, acontrol-based trailing suction hopper dredger dredging mathematics model was researchedand built, and intelligent analysis and evaluation was made to dredging process andproduction efficiency. Model-based predicative control strategy (MPC) was adopted to obtainthe best strategy for dredging optimization and to achieve dredging optimization controlunder different soil conditions and different dredging equipment.Firstly, taking the soil and other factors into account, the thesis presents a mathematicsmodel of hopper process. Besides, the thesis models and simulates drag-head and hopper andverifies these models by real measured data. The result indicates the model is of highaccuracy and can be used in MPC control design.Secondly, on the basis of system analysis and study of dredgers’ dredging process, anonline dredging optimization method developed by model predictive control was brought up.The purpose of optimization is to maximize the dredgers’ production within a completedredging cycle. MPC controller consists of three parts: mathematics model, target functionand optimizer.Dredging optimization process is a complex, multi-system coupling and amulti-constraint conditions problem. Genetic Algorithm was adapted to MPC controllers’optimizer in the thesis. Then the optimized values, the best controllable dredging parameterscan be found by optimizer in a large search room and within less time. The optimizationalgorithm and MPC controller were stimulated and validated, using the project datameasured on “Xinhai feng” trailing suction hopper dredger, and performance contrast withupdated control technology. The result shows that MPC optimization shortens the dredgingtime of10%--18%, and increases dredging efficiency of10%. Lastly, man-machine interface is developed based on LabVIEW Software. Accordingto cycle production and time efficiency, dredging performance was evaluated by system.Then the optimal dredging cycle and the best controllable parameters suitable to dredgingconditions were given in record to maximize the high dredging production and to improvethe dredging efficiency and its performance.