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A project for upgrading conventional cars to hybrid electric vehicles is presented. The project is carried out by four Italian partners within a EU project financed by the LIFE programme. Hybridisation to a through the road parallel hybrid structure is obtained by integration of wheel motors in rear wheels, the addition of an additional battery, of flexible photovoltaic panels and a Vehicle Management Unit. The technical aspects related to vehicle conversion and the results of test bench and road tests on first prototypes are presented and discussed, as well as the perspectives related to the industrialization of such system.

The growth of world energy consumption and the increase of passenger vehicles areis setting new challenges to environmental protection. Large diffusion of electric vehicles and hybrid electric vehicles seems to be the most feasible solution. However, the need of fast charging infrastructure, the still low penetration of renewable electricity production and the massive reconversion of fleets limit the feasibility of this solution.

The diffusion of electric vehicles (EVs) is strongly limited by charging issues, among which there is their potential impact on electricity grid network. This paper analyzes the possible recourse to recharging stations based on compressed air energy storage (CAES) system with volumetric compressor and expander as a solution for reducing the impact of recharging stations on power system operation.

This paper presents a comparison between two identification methods for the off-line identification of non linear models of Unmanned Underwater Vehicles (UUV's), one based on the minimization of the acceleration prediction error (classic method) and another based on the minimization of the velocity one step prediction error (new method). Both methods are compared through its application to the identification of the dynamic model of URIS UUV. Results suggest that better models can be obtained using the proposed method

This paper deals with the identification of multivariable models of underwater vehicles. The proposed method can be applied to non linear models that are linear with respect to the unknown parameter vector. In order to cope with time-varying parameters, a recursive version ofthe identification algorithm has been implemented. The validity of this algorithm is illustrated by an application to the identification ofthe longitudinal dynamics of a DSRV(Deep Submergence Rescue Vehicle).

This paper presents an identification method for the off-line identification of non linear models of Unmanned Underwater Vehicles (UUV's). The proposed method can be applied to a quite general class of non linear multivariable models and is characterised by an excellent numerical performance. The validity of the method is demonstrated through an application to the identification of the dynamic behaviour of the URIS underwater robotic vehicle.

In this paper the problem of ship trajectory tracking with ॉ-accuracy is considered. Results presented are based on a three-degrees-of-freedom model with full dynamic interaction between motions in roll, sway and yaw. The aim of control is to minimise the influence of wave disturbances on roll motion and to track with ॉ-accuracy a ship trajectory. For this purpose a linearised and a non-linear ship model are used. The Matlab-Simulink simulations confirm the viability of the proposed method for the high precision control of ship track-keeping.

In this paper, the design of course-keeping autopilot, is based on the cognitive modelling approach and fuzzy set theory. Using this modelling approach, the aim is to design a control system based on a model of the expert, who is able to specify the general properties of the system, rather than on a model of the system to be controlled. After an heuristic description of the desired manoeuvre characteristics, fuzzy set theory is used for the synthesis of the autopilot. Simulations on a non-linear model of a containership are presented for course-keeping manoeuvres in the presence of wave disturbances.

During the last few years, fuzzy controllers have emerged as a viable alternative to others conventional control design techniques. The primary advantage of fuzzy systems, is the ability to manipulate uncertainties and expert knowledge. In this paper, the problem of designing an indirect adaptive fuzzy autopilot for a containership is addressed. The proposed adaptation mechanism is based on the interpretation of a performance function by a fuzzy system, which in turn will infer the change (adaptation) in the fuzzy controller parameters. The main advantages of using a fuzzy system to perform the adaptation with respect the use of a classical adaptation algorithms (i.e. back-propagation, least-square, MIT rule etc.), is related to the amount and form of a-priori information needed about the system to be controlled. The speed with which the adaptation is achieved makes this approach viable for on-line implementation. Simulations on a non-linear model of a containership are presented for both course-changing and course-keeping manoeuvres in the presence of environmental disturbances.

In this paper the problem of ship trajectory tracking is considered for a containership described by a three-degrees-of-freedom coupled multivariable model. The aim of control is to track with ε-accuracy a ship trajectory and to reduce the effect of waves disturbances on roll motion. For this purpose a linearized and a nonlinear ship model are used. Some simulation results are shown, which have been obtained by using a Matlab-Simulink computer program. Such results confirm the validity of the proposed high precision control algorithm, in the case of both linear and non-linear ship model.

This paper describes The development of fuzzy controller for integrated control of the coupled yaw-roll motion of a container ship. The basic fuzzy rules used for designing an integrated multivariable rudder roll damping and yaw autopilot are discussed. A random search learning mechanism for automatically tuning the fuzzy controller parameters is presented. The efficiency and robustness of the controller are evaluated by a number of simulation tests conducted at different sea states. The results are used to evaluate the performance of the fuzzy controller in comparison with a conventional LQ design.

This paper deals with the problem of determining dynamic stochastic models for two different types of naval crafts, viz. conventional ships and surface-piercing hydrofoils. For conventional ships, a single-input singleoutput model describing the heading response to the rudder is identified from experimental data collected from a hydrograPhic vessel equipped with a minicomputer system. For surface-piercing hydrofoils, a multi variable model descri bing the roll and pitch response to the four flap actions is identified from experimental data recorded during special sea trials carried out by a microprocessor- based system. In both cases a number of identification techniques are used and their corresponding results are discussed and compared. In parti cular, maximum-likelihood identification is used in two different forms: first, by considering inputs and outputs as separate processes; and, next, as a single joint process. Moreover, preliminary identification of model order and structure is carried out by using several statistical and informational crite ria recently proposed in the literature.

This paper presents and integrated software package for multivariable identification, design and control of marine systems, which has been developed for educational as well as for research purposes. The package architecture and the underlying design philosophy are illustrated together with a number of examples of identification and control of different types of ships from sea trials and from simulation experiments.

This paper describes an application of reliability techniques to assure a satisfactory availability and safety to an RCS (Ride Control System) for a new type of submerged foil hydrofoil craft. After a description of the hardware and software architecture developed for this purpose, an analysis of some reliability methods potentially capable of improving safety is carried out. A presentation of a “safety redundancy” approach, which is based on the FMEA (Failure Mode and Effects Analysis) method applied to RCS is given. The simulation experiments carried out indicate that a substantial safety improvement can be achieved.

This paper seals with the identification experiments carried out in the recent vears by the L.A.N. (Ship Automation Laboratory) for determining a suitable mathematical model of the ship steering dynamics. Some basic concepts concerning the mathematical models are discussed, and the experimental methods as well as the results achieved by sea—trials performed on board of two shies with definitely different characteristics are presented. Such results, obtained after analysing a very large member of fall-scale experiments, confirm the adequacy of an A.R.M.A. model for describing the ship steering process.

This paper deals with the application of identification methods to the determination of the dynamical behaviour of an UUV (Unmanned Underwater Vehicle). After a concise introduction to the longitudinal equations of the motion, which describe the heave and pitch responses to the action of the control surface deflections and of the thrusters, identification is formulated for a linearized UUV model. The related minimization problem is approached and solved by means of two different random-search methods, respectively based on simulated annealing and on genetic algorithms. The numerical features of such identification methods are discussed and some preliminary promising results are presented, which are obtained by simulation experiments.

This paper deals with the identification of hydrodynamic derivatives in a Remotely Operated Vehicle (ROV). In-water tests have been performed to identify yaw inertial and drag coefficients on the prototype ROV Roby2, developed at IAN. Vehicle heading has been measured losing a commercial digital fluxgate compass. A least square off line polynomial data fitting technique to compute signal derivatives is applied to experimental compass data. Subsequently, least squares and random-search algorithms are applied to experimental data to identify the vehicle model's coefficients. The experimental set-up and modalities are discussed and results of the least squares and random-research algorithms are compared.

A criterion for performing the controlled maintenance is proposed in this paper.

Among the researches performed on board M/s “Esquilino”, an experiment has been planned in order to study the optimization of the course holding prosess.

A method is proposed for the marine collision avoidance problem which has been developed for implementing anticollision systems on computerized radar units. Such method allows us to determine a solution of kinematic type for the collision avoidance problem consisting of a sequence of evasive optimal manoevres each of which is computed according to the rules as in IMCO text of the 1972 Conference.

A number of research studies have been carried out, during the last two decades, devoted to rudder roll stabilization of surface ships. A possible solution has been recently proposed, which is based on adaptive control. According to such approach, the unknown system parameters are recursively estimated by an RPE (Recursive Prediction Error) method for a linear or nonlinear rudder-yaw and rudder-roll dynamical model, while an LQG (Linear Quadratic Gaussian) optimal control strategy is subsequently applied in order to minimize a preset cost function associated to the dynamical process. It should be pointed out, however, that in the presence of a relatively high number of unknown parameters, RPE method can give rise to time consuming numerical algorithms with a too low convergence rate.