RADIAL BASIS FUNCTION ARTIFICIAL NEURAL NETWORKS AND FUZZY LOGIC

This paper examines the underlying relationship between radial basis function artificial neural networks and a type of fuzzy controller. The major advantage of this relationship is that the methodology developed for training such networks can be used to develop 'intelligent' fuzzy controlers and an application in the field of robotics is outlined. An approach to rule extraction is also described. Much of Zadeh's original work on fuzzy logic made use of the MAX/MIN form of the compositional rule of inference. A trainable/adaptive network which is capable of learning to perform this type of inference is also developed

Radial Basis Function Artificial Neural Networks and Fuzzy Logic

this paper we examine the Radial Basis Function (RBF) artificial neural network and its application in the approximate reasoning process. The paper opens with a brief description of this type of network and its origins, and then goes on to show one way in which it can be used to perform approximate reasoning. We then consider the relation between a modified form of RBF network and a fuzzy controller, and conclude that they can be identical. Applications to mobile robot navigation will be described. The final part of the paper will consider a novel type of network, the Artificial Neural Inference (ANI) network, which whilst related to the RBF network, can perform max/min compositional inference. Making use of some new results in analysis, it is also possible to propose an adaptive form of this network, and it is on this network that our current work is focused

A Switching Control Perspective on the Offshore Construction Scenario of Heavy-Lift Vessels

Position control for heavy-lift construction vessels is crucial for safe operation during offshore construction. During the various phases of a typical offshore construction assignment, considerable changes in the dynamics of the crane-vessel system occur. Operational hazard was reported if such interchanging dynamics are not properly handled. However, to date and the best of our knowledge, no systematic control solution is reported considering multiphase offshore construction scenarios. This article proposes a switched dynamical framework to model the interchanging phases and to formulate a comprehensive position control solution for heavy-lift vessels. Stability and robustness against modeling imperfections and environmental disturbances are analytically assessed. The effectiveness of the solution is verified on a realistic heavy-lift vessel simulation platform; it is shown that the proposed switched framework sensibly improves accuracy and reduces hazard compared with a nonswitched solution designed for only one phase of the construction scenario.Accepted Author ManuscriptMarine and Transport TechnologyShip Design, Production and OperationsTeam DeSchutte

Design, Control, and Applications of Autonomous Mobile Robots

Introduction An autonomous robot is a machine that operates in a partially unknown and unpredictable environment. In contrast to robots used in manufacturing plants, where the environment is highly controlled, autonomous robots cannot always be programmed to execute predefined actions because one does not know in advance what will be the universe of required sensorimotor transformations required by the various situations that the robot might encounter. Furthermore, the environment might have dynamic characteristics that require rapid online modifications in the robot behaviour. For these reasons, in the last ten years several researchers have looked at novel methods for setting up autonomous mobile robots. The basic idea behind most approaches is to break down sequential topdown programs into a set of simple, distributed, and decentralised processes that have direct access to sensors and motors of the robot. The first formalisation of this approach is the subsumption architec

Construction mode detection for autonomous offshore heavy lift operations

Offshore platforms and windmills are constructed by assembling huge mechanical structures transported by heavy lift vessels. The construction process comprises two interconnected operations, the dynamic positioning (DP) of the vessel and the lifting of heavy loads. The DP system is commonly designed and tuned for the case that there is no load or for the case that the heavy load is free-hanging (mode 1). During the transition from the free-hanging to the case that the vessel is connected to a heavy load which is mounted to the platform (mode 2), the DP system may not be able to preserve the position stability of the vessel, jeopardizing human and system safety. The goal of this work is to design an intelligent monitoring system for the early detection of the transition between the two construction modes by adopting a nonlinear state estimation approach. Simulation results are used for illustrating the effectiveness of the proposed construction mode detection system.Transport Engineering and Logistic

Observer-based robust control for dynamic positioning of large-scale heavy lift vessels

With the growing demand of large-scale heavy lift vessels in the deep-sea offshore construction works, high performance of Dynamic positioning (DP) systems is becoming ever crucial. However, current DP systems on board of heavy lift vessels do not consider model uncertainty (typically arising from mooring forces). In this paper, an observer-based robust controller is designed that can tackle model uncertainty in hydrodynamic damping and mooring forces, environmental disturbances as well as can filter out the high-frequency vessel movement. Closed-loop system stability is analytically established in terms of uniformly ultimately boundedness. In addition, several key performance indicators are provided for tuning the performance of the controller. The effectiveness of the proposed control framework is studied in simulation with a crane-vessel system.Marine and Transport TechnologyShip Design, Production and OperationsTeam DeSchutte

Ship diesel engine performance modelling with combined physical and machine learning approach

Condition Based Maintenance on diesel engines can help to reduce maintenance load and better plan maintenance activities in order to support ships with reduced or no crew. Diesel engine performance models are required to predict engine performance parameters in order to identify emerging failures early on and to establish trends in performance reduction. In this paper, a novel approach is proposed to accurately predict engine temperatures during operational dynamic manoeuvring. In this hybrid modelling approach, the authors combine the mechanistic knowledge from physical diesel engine models with the statistic knowledge from engine measurements on a sound engine. This simulation study, using data collected from a Holland class patrol vessel, demonstrates that existing models cannot accurately predict measured temperatures during dynamic manoeuvring, and that the hybrid modelling approach outperforms a purely data driven approach by reducing the prediction error during a typical day of operation from 10% to 2%

Ship diesel engine performance modelling with combined physical and machine learning approach

Condition Based Maintenance on diesel engines can help to reduce maintenance load and better plan maintenance activities in order to support ships with reduced or no crew. Diesel engine performance models are required to predict engine performance parameters in order to identify emerging failures early on and to establish trends in performance reduction. In this paper, a novel approach is proposed to accurately predict engine temperatures during operational dynamic manoeuvring. In this hybrid modelling approach, the authors combine the mechanistic knowledge from physical diesel engine models with the statistic knowledge from engine measurements on a sound engine. This simulation study, using data collected from a Holland class patrol vessel, demonstrates that existing models cannot accurately predict measured temperatures during dynamic manoeuvring, and that the hybrid modelling approach outperforms a purely data driven approach by reducing the prediction error during a typical day of operation from 10% to 2%.</p