Gianguido Rizzotto

Automatic synthesis, analysis and implementation of a fuzzy controller

A method based on the cell-to-cell approach is proposed for extracting the control rules for a fuzzy controller. This procedure was implemented to control the position of a DC motor. To obtain high performance in terms of fuzzy inferences per second (FIPS) that complex control requires, the rules and the membership functions have been implemented via a hardware solution, WARP. WARP is a dedicated VLSI machine with an architecture designed to efficiently exploit data provided by the cell-to-cell approach. The global architecture along with some generalities on the particular approach are presented.<<ETX>>

Soft computing for the intelligent robust control of a robotic unicycle with a new physical measure for mechanical controllability

Abstract The posture stability and driving control of a human-riding-type unicycle have been realized. The robot unicycle is considered as a biomechanical system using an internal world representation with a description of emotion, instinct and intuition mechanisms. We introduced intelligent control methods based on soft computing and confirmed that such an intelligent control and biological instinct as well as intuition together with a fuzzy inference is very important for emulating human behaviors or actions. Intuition and instinct mechanisms are considered as global and local search mechanisms of the optimal solution domains for an intelligent behavior and can be realized by genetic algorithms (GA) and fuzzy neural networks (FNN) accordingly. For the fitness function of the GA, a new physical measure as the minimum entropy production for a description of the intelligent behavior in a biological model is introduced. The calculation of robustness and controllability of the robot unicycle is presented. This paper provides a general measure to estimate the mechanical controllability qualitatively and quantitatively, even if any control scheme is applied. The measure can be computed using a Lyapunov function coupled with the thermodynamic entropy change. Interrelation between Lyapunov function (stability condition) and entropy production of motion (controllability condition) in an internal biomechanical model is a mathematical background for the design of soft computing algorithms for the intelligent control of the robotic unicycle. Fuzzy simulation and experimental results of a robust intelligent control motion for the robot unicycle are discussed. Robotic unicycle is a new Benchmark of non-linear mechatronics and intelligent smart control.

A fuzzy decision directed filter for impulsive noise reduction

Abstract In this paper a fuzzy filter for the impulsive noise reduction is presented. A fuzzy system has been developed to detect noise configurations and then a classical filter is activated depending on the output of a structure extraction process.

Fuzzy controller design to drive an induction motor

This paper describes the simulation results of an induction motor speed control based on fuzzy logic theory. Voltage impress control technique has been applied to medium power induction motor. An automatic approach to extract control rules allows us to reduce the design time. Computer simulations have been carried out in order to test the performances of the whole control system. Then a hardware implementation of the fuzzy controller is proposed by means of a dedicated fuzzy processor: WARP (weight associative rule processor).<<ETX>>

Adaptive fuzzy filtering for audio applications using a neuro-fuzzy modelization

The paper describes a new denoising technique particularly suited for audio signals affected by white noise. The filtering algorithm is based on adaptive fuzzy rules taking into consideration the local temporal signal characteristics in order to estimate the noise components and consequently eliminate them. For a correct initial setting of the membership functions parameters describing the variables involved in the fuzzy processing, a pre-processing phase based on a neuro-fuzzy network has been implemented. The results of this nonlinear approach compared with classical filtering techniques are found to be attractive especially for non-stationary signals.

Neuro-fuzzy Networks

One of the most important research themes, in the sense of intelligent processing techniques hybridization, is the neuro-fuzzy approach. The birth of this kind of system is mostly connected with the attempt to unify the advantages of neural and fuzzy techniques using one hybrid architecture only, often referred to as fuzzy neural networks (FNN).

Fuzzy logic based image processing in IQTV environment

In this paper, new filtering techniques for TV quality improvement are presented. Fuzzy logic has been used to develop non-linear filters for impulsive and Gaussian noise reduction, scanning rate conversion and detail visibility enhancement. >

Fuzzy Cellular Neural Networks

In this, as in the previous chapter, we will deal with information processing systems that were originally inspired by the concepts underlying soft computing. The integration of fuzzy logic concepts in a widely spread architecture such as that of cellular neural networks in fact led to the birth of fuzzy CNNs.

Soft computing simulation design of intelligent control systems in micro-nano-robotics and mechatronics

Abstract The soft computing simulation design methodology of intelligent control system for mobile micro-nano-robots based on modeling of non-linear dissipative equations of robots motion with a minimum entropy production is described. It includes hierarchical levels for description of dynamic behavior of mobile micro-nano-robots based on laws of microphysics, quantum logic of intelligent dynamic behavior of control objects, optimal control of states and dynamic system theory of mechanical motion. The description of a thermodynamic intelligent behavior (with minimum entropy production) of control objects (robots) and their interrelations with Lyapunov stability conditions are introduced. The role of soft computing on the basis of GA with a fitness function as a minimum entropy production for intelligent control of mobile micro-nano-robots is discussed.

DC/DC converters fuzzy control

This paper aims to illustrate the use of a sophisticated methodology, based on system simulation techniques, to design a fuzzy controller for a DC/DC converter. The goal of the control is to stabilize the voltage output of the switching regulator against the load and parametric variations for a flyback topology. This system is characterized by being unstable in closed loop configuration. Robustness and high performances art requested to the control law especially for high power systems (more than 200 Watt). In order to obtain a good cost/performances ratio, the rules and the related membership functions have to be implemented via an analog dedicated solution capable to obtain an elevated number of FIPS (fuzzy inference per second).<<ETX>>