Cesare Fantuzzi

On the approximation capabilities of the homogeneous Takagi-Sugeno model

The approximation capability of the Takagi-Sugeno model are investigated in this paper. This kind of model features a functional consequent which is usually a first-degree polynomial in the inputs. The importance of the constant term in such a polynomial is highlighted showing that if it is discarded the Takagi-Sugeno model features the same approximation power of the simpler constant-consequence systems.

A novel theory for sampled data system passivity

This paper presents a novel approach to the interconnection of a continuous time and a discrete time physical system. This is done in a way which preserves the passivity of the coupled system independently of the sampling time. A direct application in the field of haptic displays, where a virtual environment should feel like equivalent physical systems, is presented.

Diagnosis techniques for sensor faults of industrial processes

A model-based procedure exploiting analytical redundancy for the detection and isolation of faults in input-output control sensors of a dynamic system is presented. The diagnosis system is based on state estimators, namely dynamic observers or Kalman filters designed in deterministic and stochastic environments, respectively, and uses residual analysis and statistical tests for fault detection and isolation. The state estimators are obtained from an input-output data process and standard identification techniques based on ARX or errors-in-variables models, depending on signal to noise ratio. In the latter case the Kalman filter parameters, i.e., the model parameters and input-output noise variances, are obtained by processing the noisy data according to the Frisch scheme rules. The proposed fault detection and isolation tool has been tested on a single-shaft industrial gas turbine model. Results from simulation show that minimum detectable faults are perfectly compatible with the industrial target of this application.

A SysML-Based Methodology for Manufacturing Machinery Modeling and Design

This paper describes a modeling methodology to support the design process of complex systems. The main challenge in modern industrial applications is the sheer volume of data involved in the design process. While using high-level abstraction is necessary to manage this data and analyze the system “as a whole,” designers need also to retain all the low-level information of the system, in order to be able to perform optimizations and modifications at later times. The solution proposed here is to use a hierarchy of models, each one describing the system at different levels of abstraction, and arrange them in such a way that it is possible to easily “map” each level onto the others. The topmost layer of the system description is expressed in System Modeling Language, a general-purpose modeling language based on Unified Modeling Language.

Fault diagnosis in power plant using neural networks

Abstract Fault diagnosis and identification (FDI) have been widely developed during recent years. Model-based methods, fault tree approaches and pattern recognition techniques are among the most common methodologies used in such tasks. Neural networks have been used in FDI problems for model approximation and pattern recognition as well. However, because of difficulties to perform Neural Network training on dynamic patterns, the second approach seems more adequate. In this paper, the FDI methodology consists of two stages. In the first stage, the fault is detected on the basis of residuals generated from a bank of Kalman filters, while, in the second stage, fault identification is obtained from pattern recognition techniques implemented by Neural Networks. The proposed fault diagnosis tool has been tested on a model of a power plant and results from simulations are reported and commented in the paper.

Arbitrarily shaped formations of mobile robots: artificial potential fields and coordinate transformation

In this paper we describe a novel decentralized control strategy to realize formations of mobile robots. We first describe how to design artificial potential fields to obtain a formation with the shape of a regular polygon. We provide a formal proof of the asymptotic stability of the system, based on the definition of a proper Lyapunov function. We also prove that our control strategy is not affected by the problem of local minima. Then, we exploit a bijective coordinate transformation to deform the polygonal formation, thus obtaining a completely arbitrarily shaped formation. Simulations and experimental tests are provided to validate the control strategy.

Digital passive geometric telemanipulation

In this paper we present an intrinsically passive telemanipulation scheme over a digital transmission line Internet-like. We present an analysis of the energetic behavior of the communication line both in case of loss of packages and in case of variable delay. The sample data nature of the passive controller is explicitly taken into account following the approach outlined.

High-speed DSP-based implementation of piecewise-affine and piecewise-quadratic fuzzy systems

Abstract The paper tackles the problem of executing high-dimensional fuzzy inferences following zeroth- and first-order Takagi–Sugeno inference models. The choice of a proper AND operator, which is compatible with all the semantic requirements for a conjunctive aggregation, results in an input–output relationship which is piecewise affine or quadratic. The proposed inference procedures exploit this to perform the computation from inputs to output in a time that does not grow exponentially with the number of inputs. Some details of the implementation of the two inference procedures on a TMS320C6201 are given along with some simulation results demonstrating the effectiveness of the piecewise approach.

AGV global localization using indistinguishable artificial landmarks

In this paper we consider the global localization problem for an industrial AGV moving in a known environment. The problem consists of determining the pose of the vehicle without any prior information about its location. The vehicle is supposed to be equipped with a laser scanner that allows to measure the range and bearing of the vehicle with respect to a set of anonymous landmarks. A map with the positions of all landmarks in the environment is available to the localization system. We propose a novel algorithm for AGV self-localization based on landmarks identification that can take into account also false detections, very common in industrial environments. The pose is computed with a single scan (2D), without any sensor fusion. The performance of the proposed strategy is shown both by simulations and experiments on real industrial plants.

Design and verification of mechatronic object-oriented models for industrial control systems

The paper describes a methodological framework that aims to apply formal design and verification techniques in the development of industrial control systems, with particular regard to the domain of manufacturing machines. The methodology is based on an object-oriented approach, revisited in a mechatronic perspective, and is supported by the specification methods proposed by the UML language. This language, opportunely adapted to the application domain, permits to describe control system design models which are modular, reusable and independent from the implementation architecture. The implementation domain is taken into account only during the verification phase, in which it is necessary to identify a correct semantical interpretation according to the execution model of the computational platform. In particular, the different frameworks of the industrial standards IEC 61131-3 and IEC 61499 are considered and compared with each other. The paper shows that formal verification techniques can be applied to prove the correctness of the design specification or to guide its iterative refinement, thanks to a translation of the O-O model into the input language of the model checking tool SMV.