Development of a semi-active car suspension control system using magneto-rheological damper model

In this paper, the development of a semi- active suspension control of quarter car model using fuzzy-based controller has been done. The quarter car model to be used here can be described as a nonlinear two degrees of freedom system which is subject to excitation from different road profile. The semi-active control is designed as the fuzzy control inferred by using two single input rule fuzzy modules, and the road model is used as the control force is released by actuating an electromagnetic shaker. To implement semi-active suspension system experimentally, the MR damper is used here as the adjustable damper. The MR damper is a control device that consists of a hydraulic cylinder filled with magnetically polarizable particles suspended in a liquid. MR dampers dissipate vibration by absorbing energy. Magnetorheological (MR) fluids dampers are very effective to control vibration, which use MR fluids to produce controllable damping force and provide both the reliability of passive systems and the facility of active control systems with small power supply. Due to their mechanical simplicity, high dynamic range, low power requirements, large force capacity, and robustness, offer an attractive means of vibration protection. The objectives of this are modeling of semi-active suspension system, developing controller and understanding the characteristics of the MR damper to provide effective damping for the purpose of suspension isolation or suppression car model. In this work pid, fuzzy logic and fuzzy-hybrid controller are used to control semi-active car suspension system

A LabVIEW based data acquisition system for vibration monitoring and analysis

LabVIEW (Laboratory Virtual Instrument Engineering Workbench) is gaining popularity as a graphical programming language, especially for data acquisition and measurement. This is due to the vast array of data acquisition cards and measurement systems which can be supported by LabVIEW as well as the relative ease with which advanced software can be programmed. One area of application of LabVIEW is the monitoring and analysis of vibration signals. The analysis and monitoring of the signal are of concern for fault detection and predictive maintenance. This paper describes LabVIEW based data acquisition and analysis developed specifically for vibration monitoring and used with vibration fault simulation systems (VFSS). On-line displays of time and frequency domains of the vibration signal provide a user-friendly data acquisition interface

Vibration faults simulation system (VFSS): A lab equipment to aid teaching of mechatronics courses

VFSS is an example of a mechatronics system which involves data acquisition and analysis using LabVIEW-based virtual instrument technology. This system can serve as teaching equipment for mechatronics students in the area of data acquisition, sensors and actuators, signal processing and vibration monitoring to aid students' understanding on these subjects. Since vibration fault signals and their causes are important for fault detection and diagnosis, a vibration faults simulation system is developed to gain good understanding of such signals. To achieve this a vibration faults simulation rig (VFSR) is designed and developed to simulate and study most common vibration fault signatures encountered in rotating machines. A LabVIEW-based data acquisition system is used to acquire and analyze the fault signals. The complete system has been developed and tested and the fault signals were compared with normal signals so as to ascertain the condition of the machine under investigation. VFSS has been successfully used to demonstrate some vital concepts in the teaching of DSP, sensor and actuators and mechanical vibration since data are acquired from the physical system and are analyzed to derive information on the system under investigation. This approach further allows students to gain insight into effects of noise on measurements and how such effects can be combated

Keystroke pressure based typing biometrics authentication system by combining ANN and ANFIS-based classifiers

Security of an information system depends to a large extent on its ability to authenticate legitimate users as well as to withstand attacks of various kinds. Confidence in its ability to provide adequate authentication is, however, waning. This is largely due to the wrongful use of passwords by many users. In this paper, the design and development of keystroke pressure-based typing biometrics for individual user's verification which based on the analysis of habitual typing of individuals is discussed. The paper examines the use of maximum pressure exerted on the keyboard and time latency between keystrokes as features to create typing patterns for individual users. Combining both an Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are adopted as classifiers to verify the authorized and unauthorized users based on extracted features of typing biometric. The effectiveness of the proposed system is evaluated based upon False Reject Rate (FRR) and False Accept Rate (FAR). A series of experiment shows that the proposed system that used combined classifiers produces promising result for both FAR and FRR

Iris Recognition System Using Support Vector Machines

In recent years, with the increasing demands of security in our networked society, biometric systems for user verification are becoming more popular. Iris recognition system is a new technology for user verification. In this paper, the CASIA iris database is used for individual user’s verification by using support vector machines (SVMs) which based on the analysis of iris code as feature extraction is discussed. This feature is then used to recognize authentic users and to reject impostors. Support Vector Machines (SVMs) technique was used for the classification process. The proposed method is evaluated based upon False Rejection Rate (FRR) and False Acceptance Rate (FAR) and the experimental result show that this technique produces good performance

Design And Implementation of Dsp-Based Intelligent Controller For Automobile Braking System

An intelligent braking system has great potential applications especially, in developed countries where research on smart vehicle and intelligent highways are receiving ample attention. The system when integrated with other subsystems like automatic traction control, intelligent throttle, and auto cruise systems, etc will result in smart vehicle maneuver. The driver at the end of the day will become the passenger, safety accorded the highest priority and the journey optimized in term of time duration, cost, efficiency and comfortability. The impact of such design and development will cater for the need of contemporary society that aspires to a quality drive as well as to accommodate the advancement of technology especially in the area of smart sensors and actuators. The emergence of digital signal processor enhances the capacity and features of universal microcontroller. This paper introduces the use of TI DSP, TMS320LF2407 as an engine of the system. The overall system is designed so that the value of inter-vehicle distance from infrared laser sensor and speed of follower car from speedometer are fed into the DSP for processing, resulting in the DSP issuing commands to the actuator to function appropriately

Iris recognition system by using support vector machines

In recent years, with the increasing demands of security in our networked society, biometric systems for user verification are becoming more popular. Iris recognition system is a new technology for user verification. In this paper, the CASIA iris database is used for individual userpsilas verification by using support vector machines (SVMs) which based on the analysis of iris code as feature extraction is discussed. This feature is then used to recognize authentic users and to reject impostors. Support Vector Machines (SVMs) technique was used for the classification process. The proposed method is evaluated based upon False Rejection Rate (FRR) and False Acceptance Rate (FAR) and the experimental result show that this technique produces good performance

Fuzzy antiwindup schemes for NCTF control of Point-Topoint (PTP) positioning systems

The positioning systems generally need a controller to achieve high accuracy, fast response and robustness. In addition, ease of controller design and simplicity of controller structure are very important for practical application. For satisfying these requirements, NCTF (nominal characteristic trajectory following) controller has been proposed as a practical PTP positioning control. However, the effect of actuator saturation can not be completely compensated due to integrator windup because of plant parameter variations. This study presents a method to improve the NCTF controller for overcoming the problem of integrator windup by adopting fuzzy anti-windup schemes. Two fuzzy antiwindup schemes based on Mamdani and Takagi-Sugeno fuzzy system are developed and evaluated their effectiveness. The improved NCTF controller with the proposed fuzzy anti-windup schemes is evaluated through simulation using dynamic model of a rotary positioning system. The results show that the improved NCTF controller with Takagi-Sugeno-based fuzzy windup is the best scheme to compensate for the effect of integrator windup

Performance evaluation of music and minimum norm eigenvector algorithms in resolving noisy multiexponential signals

Eigenvector methods are gaining increasing acceptance in the area of spectrum estimation. This paper presents a successful attempt at testing and evaluating the performance of two of the most popular types of subspace techniques in determining the parameters of multiexponential signals with real decay constants buried in noise. In particular, MUSIC (Multiple Signal Classification) and minimum-norm techniques are examined. It is shown that these methods perform almost equally well on multiexponential signals with MUSIC displaying better defined peaks

Parameter Estimation of Transient Multiexponential Signals Using SVD-ARMA and Multiparameter Deconvolution Techniques

Much has been reported about the analysis of transient multiexponentials data. In a previous paper, for example, this analysis was done using autoregressive moving average model which was applied to the deconvolved data arising from the application of Gardner transform followed by optimal compensation deconvolution to the original signal. Optimal compensation deconvolution uses a single parameter noise-reduction parameter. In this paper, a deconvolution parameter incorporating multiple noise-reduction parameters is used instead. Simulations and experimental results show that the proposed combination, despite its limitations supersedes several existing methods