Alessandro Rizzo

Chaotic pulse position modulation to improve the efficiency of sonar sensors

Ultrasonic devices are widely used in robotics as exteroceptive sensors for ranging measurements. Robotic applications often involve a large number of sonars operating concurrently, giving rise to the phenomenon of crosstalk. In this work, the problem of improving performance of ultrasonic devices in the presence of crosstalk and noise is addressed. In order for each device to discriminate its own echo, chaos is exploited to create unique firing sequences. In particular, the firing scheme described in this work is inspired to a modulation scheme used in chaotic communications, called chaotic pulse position modulation (CPPM). The evaluation of the time of flight is performed by a detection filter. The experimental setup consists of a Polaroid 600 electrostatic transducer driven by a continuous CPPM modulator. Experimental results confirm the suitability of the approach.

Using the task function approach to avoid robot joint limits and kinematic singularities in visual servoing

We propose in this paper solutions to avoid robot joint limits and kinematic singularities in visual servoing. We use a control scheme based on the task function approach. It combines the regulation of the selected vision based task with the minimization of a secondary cost function, which reflects the manipulability of the robot in the vicinity of internal or external singularities. Several methods are proposed to avoid joint limits and a comparison between them is presented. We have demonstrated on various experiments the validity of our approach.

Soft analyzers for a sulfur recovery unit

This work deals with the design and implementation of soft sensors for a Sulfur Recovery Unit (SRU) in a refinery. Soft sensors are mathematical models able to emulate the behavior of existing sensors on the basis of available measurements. In this application, they are used when sensors are taken off for maintenance. The measurements considered in this work are very important for the environmental impact of the refinery, as they regard pollutant acid gas emissions. Four strategies have been implemented and compared: Multi-Layer Perceptrons (MLP) and Radial Basis Function neural networks, Neuro-Fuzzy networks and nonlinear Least-Squares (LSQ) fitting. The best performance is given by MLP neural networks and nonlinear LSQ, all of them implementing Nonlinear Moving Average models. The best soft sensors have been installed on the on-line distributed control systems of the refinery and on-line performance is highly satisfactory.

Dynamic Modeling of a Robotic Fish Propelled by a Compliant Tail

In this paper, a dynamic model for a robotic fish propelled by a tail with a flexible fin is presented. The robotic fish is composed of two links connected by an actuated joint; the frontal link is rigid and acts as the robotic fish body, while the rear link serves as the tail. The latter comprises a rigid element connected to a flexible caudal fin, whose underwater vibration is responsible for propulsion. The dynamics of the frontal link are described using Kirchhoffs equations of motion for rigid bodies in quiescent fluids. The tail vibration is modeled using Euler-Bernoulli beam theory and the effect of the encompassing fluid is described using the Morison equation. The thrust production is assessed from static thrust data in terms of the fin-tip displacement; other salient model parameters are estimated through a nonlinear least squares technique. The model is validated against experimental data on circular and S-shaped trajectories. The model can be used for simulation, prediction, design optimization, and control, as it allows for the description of the robots motion as a function of the unique input of the system, that is, the servomotor angle. Within the latter application, a heading control algorithm, in which the controller is tuned on the basis of the dynamic model, is presented.

SELF-ORGANIZATION IN NONRECURRENT COMPLEX SYSTEMS

In this paper, systems formed by networks of simple nonlinear cells are studied. Using lattice models, some of the fundamental features of complex systems such as self-organization and pattern formation are illustrated. In the first part of this work, a lattice of identical Chuas circuit is used to experimentally study its global spatiotemporal dynamics, according to the variation of some macroparameters, like the coupling coefficient or the neighboring dimension. The second part of the paper deals with the remarkable improvements regarding regularization and pattern formation, obtained in networks of nonlinear systems by introducing some spatial diversity, especially generated by deterministic, unpredictable dynamics. Simulation results show that synchronization and self-organization occur in networks with a few nonlocally connected cells, with irregular topology and small spatial diversity.

Kriging metamodel management in the design optimization of a CNG injection system

This paper deals with the use of Kriging metamodels in multi-objective engineering design optimization. The metamodel management issue to find the tradeoff between accuracy and efficiency is addressed. A comparative analysis of different strategies is conducted for a case study devoted to the design of a component of the injection system for Compressed Natural Gas (CNG) engines. The computational results are reported and analyzed for a performance assessment conducted with a data envelopment analysis approach.

Dynamical network interactions in distributed control of robots

In this paper the dynamical network model of the interactions within a group of mobile robots is investigated and proposed as a possible strategy for controlling the robots without central coordination. Motivated by the results of the analysis of our simple model, we show that the system performance in the presence of noise can be improved by including long-range connections between the robots. Finally, a suitable strategy based on this model to control exploration and transport is introduced.

An innovative intelligent system for sensor validation in tokamak machines

A sensor validation strategy based on soft computing techniques to isolate and classify some faults occurring in the measurement system of a Tokamak fusion plant is described. Particular attention is focused on the system used to measure vertical stress in the mechanical structure of a Tokamak nuclear fusion plant during fusion experiments. The strategy adopted is based on a modular structure comprising two stages. The first stage consists of a neural network which acts as a symptom model able to estimate directly some suitable features of the expected sensor responses, thus allowing the most frequently occurring sensor faults to be isolated. The second stage consists of a fault classifier implemented via a fuzzy inference system, in order to exploit the knowledge of the experts. The proposed strategy was validated at the Joint European Torus (JET), on several experiments. A comparison was made with both traditional sensor monitoring techniques and validation performed manually by experts. A great improvement was achieved, in terms of both fault detection and classification capabilities, and the degree of automation achieved.

A control-oriented model of a Common Rail injection system for diesel engines

This paper presents a model of a Common Rail injection system for diesel engines. The model is derived by considering the components of the system as control volumes and applying elementary fluid dynamics and mechanics laws. Suitable simplifications are introduced, to make the model adequate for control purposes, trading off between computational effort and accuracy. The model obtained is a fifth order nonlinear one, in state-space representation, and relies on simple, well defined, geometric parameters of the system. The results obtained are compared with data collected on an experimental setup, and with those obtained through the fluid dynamic simulation AMESIMreg

Decentralized parameter estimation and observation for cooperative mobile manipulation of an unknown load using noisy measurements

In this paper, a distributed approach for the estimation of kinematic and inertial parameters of an unknown rigid body is presented. The body is manipulated by a pool of ground mobile manipulators. Each robot retrieves a noisy measurement of its velocity and the contact forces applied to the body. Kinematics and dynamics arguments are used to distributively estimate the relative positions of the contact points. Subsequently, distributed estimation filters and nonlinear observers are used to estimate the body mass, the relative position between its geometric center and its center of mass, and its moment of inertia. The manipulation strategy is functional to the estimation process, and is suitably designed to satisfy nonlinear observability conditions that are necessary for the success of the estimation. Numerical results corroborate our theoretical findings.