Design of Optimized Fuzzy logic Controller for Magnetic Levitation Using Genetic Algorithms

This paper presents an optimum approach for designing of fuzzy controller for nonlinear system using Genetic Algorithms (GA). In this paper, a magnetic levitation system is considered as a case study and the controller is designed to keep a magnetic object suspended in the air counteracting the weight of the object. Genetic Algorithm (GA) is used in this paper as optimization method that optimizes the membership, output gain and inputs gains of the fuzzy controllers. The proposed algorithms are implemented using Matlab and Simulin

FPGA fuzzy controller design for magnetic ball levitation

this paper presents a fuzzy controller design for nonlinear system using FPGA. A magnetic levitation system is considered as a case study and the fuzzy controller is designed to keep a magnetic object suspended in the air counteracting the weight of the object. Fuzzy controller will be implemented using FPGA chip. The design will use a high-level programming language HDL for implementing the fuzzy logic controller using the Xfuzzy tools to implement the fuzzy logic controller into HDL code. This paper, advocates a novel approach to implement the fuzzy logic controller for magnetic ball levitation system by using FPGA

Cooperative Throttle and Brake Fuzzy Control for ACC+Stop&Go Maneuvers

The authors are with the Industrial Computer Science Department, Instituto de Automática Industrial (CSIC), 28500 Madrid, SpainThe goal that a car be driven autonomously is far in the future and probably unreachable, but as a first step in that direction, adaptive cruise control (ACC) and Stop&Go maneuver systems are being developed. These kind of controllers adapt the speed of a car to that of the preceding one (ACC) and get the car to stop if the lead car stops. This paper presents one such system and related experiments performed on a real road with real cars. The driving system gets its input via an RTK DGPS device and communicates its positions to one another via a wireless local area network link. It outputs signals controlling the pressure on the throttle and brake pedals. The control system is based on fuzzy logic, which is considered best to deal with processes as complex as driving. Two mass produced Citroën Berlingo electric vans have been instrumented, providing them with computer controlled actuators over the brake and the throttle to achieve human-like driving. The results of the experiments show that the behavior of the vehicles is very close to human and that they adapt to driving incidences, increasing the safety of the driving and permitting cooperation with manually driven cars.This work was supported in part by the Spanish Ministry of Education under Grant ISAAC CICYT DPI2002-04064-C05-02, by the Spanish Ministry of Public Works under Grant COPOS BOE 280 22/11/2002, and by the Res. 22778, Citroën España S.A. under Contract “Adquirir nuevos conocimientos sobre la introducción de las tecnologías de la información en el mundo del automóvil y para difundirlos en los ámbitos científicos, empresariales y comerciales (AUTOPIA),” and by Cybecars-2 Project UE-STREP 28062, 6th Framework Programme, 2006.Peer reviewe

Fuzzy Controller Design Using FPGA for Sun and Maximum Power Point Tracking in Solar Array System

In this paper, Two fuzzy logic controllers are fabricated on modern FPGA card (Spartan-3AN, Xilinx Company, 2009) to increase the energy generation efficiency of solar cells. These controllers are, sun tracking controller and maximum power point tracking controller. Sun tracking generating power system is designed and implemented in real time. A tracking mechanism composed of photovoltaic module, stepper motor, sensors, input/output interface and expert FLC controller implemented on FPGA, that to track the sun and keep the solar cells always face the sun in most of the day time. The proposed sun tracking controller, and maximum power point tracking controller are tested using Matlab/Simulink program, Maximum power point tracking system is designed and implemented in real time. The results show that both controllers have a response better than conventional controller applied on the same system

Comparing fuzzy and intelligent PI controllers in stop-and-go manoeuvres

The aim of this work was twofold: on the one hand, to describe a comparative study of two intelligent control techniques-fuzzy and intelligent proportional-integral (PI) control, and on the other, to try to provide an answer to an as yet unsolved topic in the automotive sector-stop-and-go control in urban environments at very low speeds. Commercial vehicles exhibit nonlinear behavior and therefore constitute an excellent platform on which to check the controllers. This paper describes the design, tuning, and evaluation of the controllers performing actions on the longitudinal control of a car-the throttle and brake pedals-to accomplish stop-and-go manoeuvres. They are tested in two steps. First, a simulation model is used to design and tune the controllers, and second, these controllers are implemented in the commercial vehicle-which has automatic driving capabilities-to check their behavior. A stop-and-go manoeuvre is implemented with the two control techniques using two cooperating vehicles

GA-based neural fuzzy control of flexible-link manipulators

The limitations of conventional model-based control mechanisms for flexible manipulator systems have stimulated the development of intelligent control mechanisms incorporating fuzzy logic and neural networks. Problems have been encountered in applying the traditional PD-, PI-, and PID-type fuzzy controllers to flexible-link manipulators. A PD-PI-type fuzzy controller has been developed where the membership functions are adjusted by tuning the scaling factors using a neural network. Such a network needs a sufficient number of neurons in the hidden layer to approximate the nonlinearity of the system. A simple realisable network is desirable and hence a single neuron network with a nonlinear activation function is used. It has been demonstrated that the sigmoidal function and its shape can represent the nonlinearity of the system. A genetic algorithm is used to learn the weights, biases and shape of the sigmoidal function of the neural network

Decoupled fuzzy sliding-mode balance control of wheeled inverted pendulums using an 8-bit microcontroller

[[abstract]]A wheeled inverted pendulum (WIP) system is a typical unstable complex nonlinear system widely utilized for educational purposes and control research. The dynamic of a WIP system can be represented as two second-order subsystems which represent the angle of body and the position of wheel. This paper proposes a decoupled fuzzy sliding-mode balance control (DFSBC) system based on a time-varying sliding surface for a WIP system. A decoupled sliding surface which includes the information of two-subsystem is designed to make the state trajectories of both subsystems move toward their sliding surface and then simultaneously approach zeros. The control effort of a WIP system is generated based on the idea that the state can quickly reach the decoupled sliding surface without large overshoot. Moreover, the slope of the decoupled sliding surface is adjusted by a fuzzy system, whose fuzzy rules are constructed based on the idea that the convergence time of the state trajectories can be reduced. Finally, an 8-bit microcontroller-based WIP system is setup. Experimental results show that the proposed DFSBC system can achieve favorable balance control response for the simultaneous control of the angle of body and the position of wheel.[[conferencetype]]國際[[conferencedate]]20120314~20120316[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Hong Kon