Estimation And Control The Overflow Loss Of Trailing Suction Hopper Dredger Based On Particle Filter

In recent years, China’s dredging projects is escalation, and dredging project isbigger and bigger. The optimization of dredging operations is not only important toimprove efficiency and precision, but also important to save labor. And TrailingSuction Hopper Dredger (TSHD) is playing a more and more important role indredging operations. As the great effect of TSHD, we must strengthen the research onit. Although modern self-propelled TSHD has installed automatic control system,there is still no decision support system to optimize the performance of TSHD wherethe conditions are not sure, such as soil types and velocity. Therefore, how to improvethe dredging ship equipment level to make the dredging output optimized and toenhance the performance and efficiency, is also the hot research spot and content ofabroad.As the dredging efficiency is affected by soil types and the technology ofoperating personnel, this paper uses particle filtering algorithm to estimate theoverflow loss of TSHD, and take actual engineering data to complete the simulation.The purpose is to improve the efficiency and production, reduce the production costand obtain higher social efficiency and the economic efficiency. Also, it provides thedecision-making support for operator’s construction.From three aspects, this paper introduces the basic theory of particle filter,including the popular predictive filtering algorithm, the recursive Bayesian estimationand Monte Carlo analysis. On the basis of the above, the common filter problems andsolving methods are discussed. Then, the particle swarm optimization algorithm isintroduced to the particle filter, and made further research.Secondly, the system models of TSHD are introduced, and emphaticallyanalyses the hopper model. As for the head model, it gives the first inhaled formula ofdensity through dynamic model. After that, the sediment process in hopper is detaileddiscussed. Then, the estimation model of overflow loss is given, as well as thecorresponding evaluation indexes to make performance evaluation. Considering theinfluence of particle size on the actual settling velocity, the sediment particle size andactual settlement speed are estimated, to improve the accuracy of overflow loss thathas been estimated.Finally, the particle filtering algorithm is applied on the estimation of overflow loss. The correctness of the estimation was illustrated by MATLAB simulation results.In the control system, the overflow losses estimated by particle filterer were feedbackto the system input through the fuzzy controller. By changing the system input tocontrol the overflow loss. The results showed that the overflow loss estimated byparticle filters could provide a very good decision-making for ship operators. And ithas practical significance.