Estimation of the soil-dependent time-varying parameters of the hopper sedimentation model: The FPF versus the BPF

A Trailing Suction Hopper Dredger (TSHD) is a ship that excavates sediments from the sea bottom while sailing. While the optimization of dredging operations is of vital importance for dredging companies the efficiency of this process is highly dependent on the detailed knowledge of the in situ soil. One of the most important processes that need to be controlled onboard the TSHD is the hopper sedimentation process, which describes the settling of the excavated material transported into the hopper. The most important soil-dependent parameter of the sedimentation process is the average grain diameter dm of the excavated soil. The accurate knowledge of the dm and the co-dependent variables such as the sand bed height hs, sand bed mass ms and the mixture density ρm is necessary to control the sedimentation process in the optimal way. These variables need to be estimated online to be integrated into the automatic controller. The main objective of this paper is to find an accurate and numerically efficient algorithm that computes the estimates of the aforementioned variables. We investigate two nonparametric filters: the benchmark Bootstrap Particle Filter (BPF) versus the recently developed Feedback Particle Filter (FPF). In the series of numerical simulations we conclude that the FPF outperforms the benchmark method in both accuracy and numerical efficiency.