A Novel Forecasting Model for the Baltic Dry Index Utilizing Optimal Squeezing

Marine transport has grown rapidly as the result of globalization and sustainable world growth rates. Shipping market risks and uncertainty have also grown and need to be mitigated with the development of a more reliable procedure to predict changes in freight rates. In this paper, we propose a new forecasting model and apply it to the Baltic Dry Index (BDI). Such a model compresses, in an optimal way, information from the past in order to predict freight rates. To develop the forecasting model, we deploy a basic set of predictors, add lags of the BDI and introduce additional variables, in applying Bayesian compressed regression (BCR), with two important innovations. First, we include transition functions in the predictive set to capture both smooth and abrupt changes in the time path of BDI; second, we do not estimate the parameters of the transition functions, but rather embed them in the random search procedure inherent in BCR. This allows all coefficients to evolve in a time-varying manner, while searching for the best predictors within the historical set of data. The new procedures predict the BDI with considerable success

Combining ILC and repetitive control to handle repeating, event-triggered disturbances in precision inkjet printing

Learning and repetitive control are powerful instruments in handling recurring disturbances. Repetitive control properly handles constantly repeating variations, while iterative learning control is well-equipped when it comes to handling event triggered deviations. Neither controller is well equipped to adequately deal with repetitive disturbances, which are only present during limited, but varying, periods of time. These are often seen in precision handling systems such as production inkjet printers. This paper combines ILC and RC using a structure which originated in multi-period repetitive control. It is shown that this enables full suppression of the repeating event-triggered disturbances. The approach is successfully demonstrated in an illustrative simulation, as well as by using experimental data from a precision inkjet printing setup

Physics-guided neural networks for inversion-based feedforward control applied to hybrid stepper motors

Rotary motors, such as hybrid stepper motors (HSMs), are widely used in industries varying from printing applications to robotics. The increasing need for productivity and efficiency without increasing the manufacturing costs calls for innovative control design. Feedforward control is typically used in tracking control problems, where the desired reference is known in advance. In most applications, this is the case for HSMs, which need to track a periodic angular velocity and angular position reference. Performance achieved by feed-forward control is limited by the accuracy of the available model describing the inverse system dynamics. In this work, we develop a physics-guided neural network (PGNN) feedforward controller for HSMs, which can learn the effect of parasitic forces from data and compensate for it, resulting in improved accuracy. Indeed, experimental results on an HSM used in printing industry show that the PGNN outperforms conventional benchmarks in terms of the mean-absolute tracking error.</p

Nonlinear Model-Based Fault Detection for a Hydraulic Actuator

This paper presents a model-based fault detection algorithm for a specific fault scenario of the ADDSAFE project. The fault considered is the disconnection of a control surface from its hydraulic actuator. Detecting this type of fault as fast as possible helps to operate an aircraft more cost effective and can help to avoid an undetected increase in fuel consumption. The method proposed here uses an Adaptive Extended Kalman Filter (A-EKF) to detect the disconnection using only local measurements (control signal to the actuator and actuator rod position). For this purpose, an accurate physical model of the hydraulic actuator is needed and the fault is detected by parameter estimation. It is shown that the A-EKF performs better than the regular Extended Kalman Filter (EKF) for this application.Control & OperationsAerospace Engineerin

Optimal Reconstruction of Flight Simulator Motion Cues Using Extended Kalman Filtering

For evaluation of simulator motion and motion platform dynamics, the motion cues generated in flight simulators need to be measured. For the SIMONA Research Simulator at TU Delft, the availability of redundant kinematic motion sensors i.e. an Inertial Measurement Unit and sensors that measure the lengths of the motion system actuators was expected to allow for optimal estimation of the flight simulator motion state using an Extended Kalman Filter. As a starting point, this sensor fusion problem was evaluated for only symmetrical simulator motion, omitting the additional asymmetrical motion states. The highly nonlinear relation between the extension of the motion base actuators and simulator position and orientation was found to require the application of an Iterative Extended Kalman Filter to ensure adequate filter convergence. Using this iterative filter, optimal estimates of the symmetrical simulator state and the IMU biases could be obtained from the two sets of redundant kinematic observations

Parameterized Iterative Learning Control: Application to a wide format inkjet printer

In this paper, a new Parameterized Iterative Learning Controller (PILC) is discussed. A fixed structure feedforward controller is put into a setting for iterative learning control and adapted to work in a repetitive environment. The fixed structure keeps the number of inline calculations low and enables proper adjustment to changing tasks. Within the ILC setting, suitable compensations can be found in a variable environment and specific penalties can be given to errors in trial domain. Further, by taking into account possible interaction between the trials, the PILC works properly with changing initial conditions over the trials. Successful implementation of the PILC on the carriage of a professional wide format inkjet printer shows its practical applicability

Ship Valuation Using Cross-Sectional Sales Data: A Multivariate Non-Parametric Approach

Despite the illiquid and heterogeneous nature of the second-hand market for bulk ships and the resulting difficulty of creating reliable time series of ship prices for generic ships, the literature on ship price dynamics relies heavily on time series models. In this paper we present, for the first time, an analysis of ship valuation using cross-sectional data based on actual sale and purchase transactions in the second-hand market for bulk ships. This allows us to investigate price formation in the second-market free of broker bias and measurement error. Moreover, we do not impose a strict linear model specification but allow for the presence of non-linearity in a flexible non-parametric vessel valuation function. Based on data from more than 1,850 individual sales of Handysize bulk carriers from January 1993 to October 2003, we find that the second-hand value of a vessel can be well described as a partially non-linear function of three main factors: DWT, age, and the state of the freight market. Maritime Economics & Logistics (2007) 9, 105–118. doi:10.1057/palgrave.mel.9100174

The Pricing of Forward Ship Value Agreements and the Unbiasedness of Implied Forward Prices in the Second-Hand Market for Ships

This paper outlines the methodology to price the newly introduced Forward Ship Value Agreements (FOSVAs). FOSVAs are derivatives aimed at managing asset risk in the second-hand markets for bulk vessels and are traded over the counter. We then estimate the implied forward prices from historical data for vessel prices and the term structure of freight rates under the assumption that the cost-of-carry relationship holds and investigate whether the implied forward prices have been unbiased predictors of realised prices. The empirical evidence rejects the unbiasedness hypothesis in all the cases studied and supports the presence of a risk premium. Maritime Economics & Logistics (2004) 6, 109–121. doi:10.1057/palgrave.mel.9100098