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Roll prediction and parameter identification of marine vessels under unknown ocean disturbances

Lee Sang-Do, Kim Hwan-Seong, You Sam-Sang, Yeon Jeong-Hum, Bui Duc Hong Phuc

Polish Maritime Research

2024

nr 1

4--15

This paper deals with two topics: roll predictions of marine vessels with machine-learning methods and parameter estimation of unknown ocean disturbances when the amplitude, frequency, offset, and phase are difficult to estimate. This paper aims to prevent the risky roll motions of marine vessels exposed to harsh circumstances. First of all, this study demonstrates complex dynamic phenomena by utilising a bifurcation diagram, Lyapunov exponents, and a Poincare section. Without any observers, an adaptive identification applies these four parameters to the globally exponential convergence using linear second-order filters and parameter estimation errors. Then, a backstepping controller is employed to make an exponential convergence of the state variables to zero. Finally, this work presents the prediction of roll motion using reservoir computing (RC). As a result, the RC process shows good performance for chaotic time series prediction in future states. Thus, the poor predictability of Lyapunov exponents may be overcome to a certain extent, with the help of machine learning. Numerical simulations validate the dynamic behaviour and the efficacy of the proposed scheme.

Optimization of stochastic production-inventory model for deteriorating items in a definite cycle using Hamilton-Jacobi-Bellman equation

Hau Bui Minh, Kim Hwan-Seong, Long Le Ngoc Bao, You Sam-Sang

LogForum

2022

Vol. 18, no. 4

397--411

Background: Inventory control is essential for a manufacturer to achieve the desired profit in successful supply chain management. This paper deals with the production-inventory system under the decrease in production rate. The model includes three stages: before the decrease in production, after the decrease in production, and after a period of inventory shortage. Throughout the stages, the stochastic inventory model is always affected by random factors and the deterioration of inventory quality. Method: The article uses the economic order quantity (EOQ) framework to evaluate costs in the production-inventory model. To optimize the manufacturer’s profit with the stochastic factor, Hamilton-Jacobi-Bellman (HJB) equation is presented to find the production rate to make the inventory model to guarantee its intended goals in a determined cycle. Result: Analytical solutions are provided for optimization of the stochastic production-inventory model. Numerical experiments show that inventory level, production rate, and profit over time are based on the optimal initial value of the production rate. Conclusion: The manufacturer’s profit comes from the stages of importing raw materials, processing and producing, storing and supplying items. Finding the initial value of the production rate can make the inventory level and production rate to ensure their desired value and get the target profit within a specified time.