Application of artificial neural networks to weighted interval Kalman filtering

The interval Kalman filter is a variant of the traditional Kalman filter for systems with bounded parametric uncertainty. For such systems, modelled in terms of intervals, the interval Kalman filter provides estimates of the system state also in the form of intervals, guaranteed to contain the Kalman filter estimates of all point-valued systems contained in the interval model. However, for practical purposes, a single, point-valued estimate of the system state is often required. This point value can be seen as a weighted average of the interval bounds provided by the interval Kalman filter. This article proposes a methodology based on the application of artificial neural networks by which an adequate weight can be computed at each time step, whereby the weighted average of the interval bounds approximates the optimal estimate or estimate which would be obtained using a Kalman filter if no parametric uncertainty was present in the system model, even when this is not the case. The practical applicability and robustness of the method are demonstrated through its application to the navigation of an uninhabited surface vehicle. © IMechE 2014

Non-linear control algorithms for an unmanned surface vehicle

Although intrinsically marine craft are known to exhibit non-linear dynamic characteristics, modern marine autopilot system designs continue to be developed based on both linear and non-linear control approaches. This article evaluates two novel non-linear autopilot designs based on non-linear local control network and non-linear model predictive control approaches to establish their effectiveness in terms of control activity expenditure, power consumption and mission duration length under similar operating conditions. From practical point of view, autopilot with less energy consumption would in reality provide the battery-powered vehicle with longer mission duration. The autopilot systems are used to control the non-linear yaw dynamics of an unmanned surface vehicle named Springer. The yaw dynamics of the vehicle being modelled using a multi-layer perceptron-type neural network. Simulation results showed that the autopilot based on local control network method performed better for Springer. Furthermore, on the whole, the local control network methodology can be regarded as a plausible paradigm for marine control system design. © 2014 IMechE

Subsea cable tracking by an unmanned surface vehicle

Subsea cable localisation is a demanding task that requires a lot of time, effort and expense. In the present paper the authors propose a methodology that is automated and inexpensive, based on magnetic detection from a small unmanned surface vehicle (USV) and the use of a batch particle filter (BPF) algorithm. A dynamic path planning algorithm for the USV is also developed so that adequate samples of the magnetic field readings can be gathered for processing by the BPF. All of these elements work together online as the cable is tracked, which was demonstrated in a simulated mission

Interval Kalman filtering in navigation system design for an uninhabited surface vehicle

This paper reports on the potential application of interval Kalman filtering techniques in the design of a navigation system for an uninhabited surface vehicle named Springer. The interval Kalman filter (IKF) is investigated for this task since it has had limited exposure for such usage. A state-space model of the Springer steering dynamics is used to provide a framework for the application of the Kalman filter (KF) and IKF algorithms for estimating the heading angle of the vessel under erroneous modelling assumptions. Simulations reveal several characteristics of the IKF, which are then discussed, and a review of the work undertaken to date presented and explained in the light of these characteristics, with suggestions on potential future improvements. © The Royal Institute of Navigation 2013

Crystal and solution structure of NDRG1, a membrane-binding protein linked to myelination and tumour suppression

Abstract N-myc downstream-regulated gene 1 (NDRG1) is a tumour suppressor involved in vesicular trafficking and stress response. NDRG1 participates in peripheral nerve myelination, and mutations in the NDRG1 gene lead to Charcot-Marie-Tooth neuropathy. The 43-kDa NDRG1 is considered as an inactive member of the α/β hydrolase superfamily. In addition to a central α/β hydrolase fold domain, NDRG1 consists of a short N terminus and a C-terminal region with three 10-residue repeats. We determined the crystal structure of the α/β hydrolase domain of human NDRG1 and characterised the structure and dynamics of full-length NDRG1. The structure of the α/β hydrolase domain resembles the canonical α/β hydrolase fold with a central β sheet surrounded by α helices. Small-angle X-ray scattering and CD spectroscopy indicated a variable conformation for the N- and C-terminal regions. NDRG1 binds to various types of lipid vesicles, and the conformation of the C-terminal region is modulated upon lipid interaction. Intriguingly, NDRG1 interacts with metal ions, such as nickel, but is prone to aggregation in their presence. Our results uncover the structural and dynamic features of NDRG1, as well as elucidate its interactions with metals and lipids, and encourage studies to identify a putative hydrolase activity of NDRG1