J. Aranda

Virtual and remote control labs using Java: a qualitative approach

This article describes a new way of teaching adopted at the Universidad Nacional de Educacion a Distancia (UNED) that uses dynamic and interactive simulations in a stand-alone or Web-based environment to permit control engineering students to do practical work at a distance. The article focuses on how this new stand-alone experimentation environment maintains a clear separation between the graphical experimentation interface, developed in Java, and the math and simulation engine. By constructing the environment in this fashion, the math engine can be replaced with a different one or with a real plant, or can even be ported to a remote server. A Web-based, multiuser virtual lab is also possible without the necessity of reprogramming the experimentation interface code. Other differences with respect to tools are the dynamic simulations, the user interactivity, the generation of new experiments as goals change, and the opportunity to practice with classical or advanced control strategies in different plants: a heat exchanger, a tank, a distillation column, or an inverted pendulum.

Fast Ferry Vertical Accelerations Reduction with Active Flaps and T-Foil

Abstract The research deals with the design and use of actuators to improve the seakeeping performances of a fast ferry. The interest of the research is now restricted to heaving and pitching motions, with heading sea. The ship has active control surfaces (flaps and T-foil). The paper presents a control-oriented model of these actuators, in SIMULINK. This model can be easily coupled with a SIMULINK model of the ship, furnishing a simulation environment for control studies. Using this facility, a study of control alternatives (moving the actuators) has been started. We include in the paper first results with conventional PID control, which are useful as reference for comparison purposes.

Relaxation by Hopfield network in stereo image matching

Abstract This paper outlines a relaxation approach using the Hopfield neural network for solving the global stereovision matching problem. The primitives used are edge segments. The similarity, smoothness and uniqueness constraints are transformed into the form of an energy function whose minimum value corresponds to the best solution of the problem. We combine two methods: (a) optimization/relaxation [1] and (b) relaxation merit [2] with the above three constraints mapped in an energy function. The main contribution is made (1) by applying a learning strategy in the similarity constraint and (2) by introducing specific conditions to overcome the violation of the smoothness constraint and to avoid the serious problem arising from the required fixation of a disparity limit. So, we improve the stereovision matching process. A better performance of the proposed method is illustrated with a comparative analysis against a classical relaxation method.

Identification of the Vertical Plane Motion Model of a High Speed Craft by Model Testing in Irregular Waves

Abstract Experiments in towing tanks are concerned with the determination of the motion transfer functions, which are obtained by testing in irregular waves. For this purpose a model of pitch and heave motions has been developed, and the identified models of the vertical plane motions of a high-speed craft are showed. Linear models are obtained for different sea states and ship speeds. A general low order model is obtained by pole-zero cancellation. So, a full model may be considered for simulation and verification, and a low order model for design. From the lowest order models, a state space model is obtained.

Improving the comfort of a fast ferry

A study was conducted to determine the effectiveness of using controlled flaps and a T-foil to smoothen a ships vertical motion while navigating in head seas. The study comprised two steps: to develop a tool for control design in the form of a computer-based simulation and to use this tool to develop satisfactory controllers. The simulation was based on mathematical models of the ship, the actuators, the waves and the seasickness effect. Since the actuators have limited action, there is a limited margin for improvement based on more sophisticated control strategies. Possible improvements of motion sickness incidence (MSI) are linked to a control strategy that exploits better synchronization with incident waves.

Optimal sensor placement for underwater positioning with uncertainty in the target location

Worldwide, there has been increasing interest in the use of Autonomous Underwater Vehicles (AUVs) to drastically change the means available for ocean exploration and exploitation. Representative missions include marine habitat mapping, pipeline inspection, and archaeological surveying. Central to the operation of some classes of AUVs is the availability of good underwater positioning systems to localize one or more vehicles simultaneously based on information received on-board a support ship or a set of autonomous surface vehicles. In an interesting operational scenario the AUV is equipped with an acoustic pinger and the set of surface vehicles carry a network of acoustic receivers that measure the ranges between the emitter and each of the receivers. Motivated by these considerations, in this paper we address the problem of determining the optimal geometric configuration of an acoustic sensor network at the ocean surface that will maximize the range-related information available for underwater target positioning. It is assumed that the range measurements are corrupted by white Gaussian noise, the variance of which is distance-dependent. Furthermore, we also assume that an initial estimate of the target position is available, albeit with uncertainty. The Fisher Information Matrix and the maximization of its determinant are used to determine the sensor configuration that yields the most accurate “expected” positioning of the target, the position of which is expressed by a probabilistic distribution. It is shown that the optimal configuration lends itself to an interesting geometrical interpretation and that the “spreading” of the sensor configuration depends explicitly on the intensity of the range measurement noise, the probabilistic distribution that defines the target position, and the target depth. Simulation examples illustrate the key results derived.

Stereo matching technique based on the perceptron criterion function

Abstract Classical stereo matching techniques use features representing objects in both images and compute the minimum difference attribute values. No knowledge of the environment is taken into account. This paper proposes an image understanding stereo matching method using a supervised networks: the perceptron.

Optimal Sensor Placement for Multiple Target Positioning with Range-Only Measurements in Two-Dimensional Scenarios

The problem of determining the optimal geometric configuration of a sensor network that will maximize the range-related information available for multiple target positioning is of key importance in a multitude of application scenarios. In this paper, a set of sensors that measures the distances between the targets and each of the receivers is considered, assuming that the range measurements are corrupted by white Gaussian noise, in order to search for the formation that maximizes the accuracy of the target estimates. Using tools from estimation theory and convex optimization, the problem is converted into that of maximizing, by proper choice of the sensor positions, a convex combination of the logarithms of the determinants of the Fisher Information Matrices corresponding to each of the targets in order to determine the sensor configuration that yields the minimum possible covariance of any unbiased target estimator. Analytical and numerical solutions are well defined and it is shown that the optimal configuration of the sensors depends explicitly on the constraints imposed on the sensor configuration, the target positions, and the probabilistic distributions that define the prior uncertainty in each of the target positions. Simulation examples illustrate the key results derived.

Decreasing of the Motion Sickness Incidence by a Multivariable Classic Control for a High Speed Ferry

Abstract The decreasing of the motion sickness in a high speed ferry produces a greater passengers comfort and safety of the vehicle. The paper presents a comparative study of the decreasing of the motion sickness incidence in a high speed ferry using different multivariable classic controllers (PD, first order filter and second order filter). The tuning of the controllers is made solving a non-linear optimization problem. The tuning method uses first a genetic algorithm and then a classic non-linear optimization algorithm.

Modelling of a High Speed Craft by a Non-Linear Least Squares Method with Constraints

Abstract A non linear least squares method with constraints have been tried. However, the extra complexity associated with non-linear systems, with constrains and no initial information of model structure, means that exhaustive search is not always feasible. In these cases genetic identification strategy can be used to obtain initial values. On the other hand, a trend in the area of system identification is to try to model the system uncertainties to fit the available analysis and design tools of robust control. The method described is applied to obtain the interval model of the vertical dynamic of a high speed craft for different speed, and comparative results obtained with the mathematical model and with a scaled model in a towing tank are showed.