The trailing suction hopper dredger (TSHD) is a ship that excavates sand and sediments from the sea bottom while sailing. Modern TSHDs are advanced ships, equipped with many local automation systems controlled from the bridge via a computer system. A comprehensive mathematical model has been developed in previous research, integrating several sub-processes. The model is used as a basis for model-based predictive control.
The main challenge for a successful adoption of the existing techniques in practice is the strongly time-varying nature of the underlying processes and disturbances. The present research project aims at the extension of the existing results to on-line (real-time) algorithms to estimate parameters that strongly vary in time, such as the soil type dependent parameters, the estimation of the states of the system and subsequently the development of adaptive and learning control methods.
The main research topic of this research project is to investigate various methods and develop new techniques for adaptive estimation and control that can be applied for the performance improvement of a hopper-dredger. The research efforts is focus on algorithm and methodology development for distributed control systems with emphasis on developing and testing methods for parameter and state estimation in an uncertain environment.