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Ferroelectric Random Access Memory (FRAM) by Texas Instruments (TI) is a non-volatile memory which allows lower power and faster data throughput compared to other non-volatile solutions. These features have accelerated the interest in this technology as the future of embedded unified memory, in particular in data logging, remote sensing and Wireless Sensor Network (WSN). The application of Model Predictive Control (MPC) in WSN has gained lot of attention in the last years and it requires solving convex optimization problems in real-time. In this paper several convex optimization algorithms have been implemented and compared on a FRAM-based MSP-EXP430FR5739 node by TI, to evaluate its suitability in extending the potentialities of onboard volatile Static Random Access Memory (SRAM) for embedded optimization-based control.

The fourth industrial age takes the manufacturing factory to a new level by introducing smart, extendible, flexible, modular and customized mass production technologies. Production lines or machines need to be integrated at the management level to be industry 4.0 compliant: in this way they can create and optimize a customer-oriented production, while constantly maintaining good performance conditions. In this context, one of the main challenges is the possibility to detect faults as fast as possible, to accurately diagnose those faults which can negatively affect the overall production cycle, and finally address them before it is too late. Due to the great importance that electric motors play in this context, an online smart algorithm for fault detection in electric motors is proposed in this paper. The effectiveness of the proposed method has been validated by applying it on an experimental benchmark, where the results show that the method is accurate and fast in detection of faults.

In this research, a localization system for a particular Automated Guided Vehicle (AGV), namely a Laser Guided Vehicle (LGV), which takes advantage of RSSI data, is described. The vehicle, its sensors and the wireless sensor network are virtualized, in order to provide a software replica of that available in a real factory. A Co-Simulator, composed by two simulation environments, has been implemented: from one hand, Robot Operating System (ROS), the RVIZ and GAZEBO for modeling and simulating the virtual system, and from the other hand, COOJA for reproducing the Wireless Sensor Network (WSN) behavior and RSSI signals. The virtualized LGV in ROS, together with its virtual sensors set, is integrated with the WSN in COOJA, so that localization can be performed by sensor fusion of odometric and RSSI data according to a Kalman Filter approach. The Co-simulator can be used to deploy path-tracking policies and test them before actual implementation in a Cyber Physical System (CPS) scenario.

Given the fact that AUV dynamics change depending on the payload, finding a mathematical model in a rather small period of time is quite important. Classical openloop identification methods give accurate parameter identification, but are also time consuming. In the paper we present an identification method based on induced self-oscillations, which has proved to be applicable to underwater vehicles. In addition to that, an error analysis for the proposed method is presented. Experimental results obtained on an underwater vehicle are given and compared to the results obtained using open-loop identification algorithms.

Once the presence of the anomaly on the bottom of the coastal sea water is confirmed it is necessary to establish if it contains explosive charge. This could be performed by using neutron sensor installed within an underwater vessel. When positioned above the object the system inspects the object for the presence of the threat materials by using neutrons from the sealed tube d+t neutron generator with detection of associated alpha particles. The detection of alpha particles defines the neutron beam which helps the reduction of the background. Whole spectrum signature could be used for the identification of threat materials investigated.

The Automarine Module is a simple cost effective method for transforming underwater remotely operated vehicles (ROVs) into autonomous underwater vehicles (AUVs), with minimum development time, and with no ROV circuit alteration. This paper presents architecture of autonomization of the VideoRay Pro II ROV together with its technical specifications. The paper also presents a procedure for open-loop identification of the nonlinear yaw model. Analytical expressions that are used for model identification are also provided.

This paper explores the forming-up, robustness of the ensuing formation and coordinated movement of autonomous, non-communicating submerged vehicles (AUV) planning their trajectories using a virtual potential fields method. The behavior and characteristic merits and problems of the proposed scheme, which plans the trajectory on the basis of AUV kinematics is tested in 2D simulations. A brief commentary on further avenues of research and improvement in order to make the method applicable to hardware-in-the-loop usage is given.

In this paper, the practical application of some well recognised fuzzy methods and neural networks techniques to modelling marine diesel engine cylinder dynamics using real-time data and expert knowledge has been considered. The simulation was done in Matlab environment with real-time data originated from 2-stroke marine diesel propulsion engine on test bed during final testing, combined with knowledge elicited from engine experts and experienced test bed operators. Takagi-Sugeno fuzzy model has been designed based on cylinder pressure data after their clustering using fuzzy subtractive method. Model parameter tuning was investigated using ANFIS with combined learning algorithms: least-squares and back-propagation gradient descent method. The model obtained can be of practical importance in engine working regime adjustment, predicting cylinder data in faulty sensor case or adaptive threshold tuning within faults detection and identification.

Guidance through waypoints is common for small autonomous marine vehicles. Guidance by the line of sight, which turns the vehicle directly towards the next waypoint without any reference path calculation, is computationally the simplest form of waypoint guidance. However, the basic algorithm gives rather poor guidance due to the missed waypoint problem, lack of sea current compensation and abrupt transitions between the consecutive waypoints. Significant path deviations and even deadlocks are possible due to these problems. Therefore, more complex algorithms are usually used in real world applications. The research reported in this paper aims to demonstrate that significant improvements of the basic line-of-sight guidance algorithm can be achieved by several intuitive, simple corrections and additions. The simplicity of the basic line-of-sight guidance algorithm is not compromised. In particular, missed waypoint detection is performed by monitoring the distance between the vehicle and the waypoint. Introduced reference heading corrections are based upon the location of the next waypoint after the one the vehicle is currently approaching, and upon the sea current direction and intensity. The results of these corrections are shown in several simulation examples. In addition, the paper includes a short discussion about the line-of-sight guidance in the diving plane.

The paper deals with marine diesel engine governor identification in normal and faulty operating conditions, using simulation method within real ship’ s propulsion plant simulator Kongsberg ERS-L11 MAN B&W-5L90MC-VLCC Version MC90-IV. Most of marine diesel engine speed controllers developed are using the PID control algorithm. PID controller parameters values: gain, integral and derivate constant, are set according to the nature and dynamic characteristics of the controlled process. These values determine the system response quality, i.e. control system quality. Determination of engine governor faults effects on system behavior and system stability was of special interest in this work. Also, engine malfunction and events that occur due to the governor failure will be presented in full paper version.