87 research outputs found
Design and implementation of fuzzy-based PID controller
controller is widely used in many industrial
applications due to its simplicity in StmctllIe and ease
of design. However, it is difficult to achieve .the
desired control performance in the presence of
unknown nonlinearities, time delays, disturbances as
well as changes in system parameters. Consequently
several PID models have been suggested so at to
alleviate these effects on the performance of the PID
controllers. One such method is based on fuzzy logic
technique which is considered much more appropri.ate
when precise mathematical formulation is infeasible or
difficult to achieve. Furthermore, some applications
such as semiconductor packaging, computer disk
drives, and ultra-precision machining require a fast
and high precision processing. Consequently, there is
the need to consider digital signal processor (DSI?)-
based fuzzy PID for use in such applications. Design
and implementation of such technique is proposed in
this paper. Results of simulation studies haw
demonstrated the feasibility of this controller since: it
produces fast response with smooth motion control
Hardware implementation of ANFIS controller for gas-particle separations in wet scrubber system
Wet scrubber system has been used for the control of gas and particulate matter (PM) emissions from production industries. Due to non-linear characteristics, wet scrubbers are limited to the control of PM that is less than 5μm. In this study, an intelligent control technique based on Adaptive Neuro-Fuzzy Inference System (ANFIS) has been designed using MATLAB software. The ANFIS Controller has the advantage of solving non-linearities in the proposed wet scrubber system by manipulating the scrubbing liquid droplet size for the effective control of particulate matter that is less than 5μm. From the simulation results, the controller was able to set PM concentration below the set-point and provides smooth control response within short settling time. Hardware implementation of the ANFIS controller was performed using prototype wet scrubber system by considering Arduino Duemilanove microcontroller and MATLAB interface. The results show that the intelligent controller has achieved the desired objectives of controlling the PM concentration effectively by setting the value below the set point (20μg/m3) which is the allowable PM concentration standard recommended by World Health Organization
Motor imagery task classification using transformation based features
tThis paper proposes a feature extraction method named as LP QR, based on the decomposition of theLPC filter impulse response matrix of the signal of interest. This feature extraction method is inspired byLP SVD and is tested in the context of motor imagery electroencephalogram. The extracted features areclassified and benchmarked against extracted features of LP SVD method. The two applied methods arealso compared regarding the required execution time, which further highlights their respective meritsand demerits. This paper closely examines the contribution of EEG channels of these two informationextraction algorithms too. Consequently, a detailed analysis of the role of EEG channels concerning thenature of the extracted information is presented. This study is conducted on the BCI IIIa competitiondatabase of four motor imagery movements. The obtained results indicate that the proposed method isthe better choice if simplicity is demanded. The investigation into the role of EEG channels reveals thatlevel of contribution each channel can be quite dissimilar for different feature extraction algorithms
RFID-based intelligent books shelving system
Searching and sorting misplaced books is a difficult task often carried out by the library personnel. Quite often, librarians are busy with searching misplaced books which are left in wrong locations by library users. It is quite difficult and almost impractical to place back all books to their assigned locations daily. To overcome this, Radio Frequ ency Identification(RFID) based Intelligent shelving system has been proposed to provide an efficient mechanism of books management monitoring through wireless communication between the RFID reader and the books. It is quite essential for the proposed system to have a smooth motion for the RFID reader during the shelving operation; otherwise acquired data will have no value due to inconsistency in reading the tags. Consequently, in this
paper, the performance of RFID reader motion and tags data
management such as retrieving information, matching with
database, sorting out the order and displaying the status of
books locations are discussed. A prototype consisting of
monitoring PC with embedded controller, two dc motors with
drivers, RFID reader and aluminum frame stick on rack have
been developed. The performance of the proposed system has
been investigated and found to be satisfactory. And it has a lot of potential applications, especially in its ability to alleviate the intensive labors and efforts in shel ving library books
Machine condition monitoring and fault diagnosis using spectral analysis techniques
There is need to continuously monitor the conditions of complex, expensive and
process-critical machinery in order to detect its incipient breakdown as well as to
ensure its high performance and operating safety. Depending on the application,
several techniques are available for monitoring the condition of a machine. Vibration
monitoring of rotating machinery is considered in this paper so as develop a selfdiagnosis
tool for monitoring machinesโ conditions. To achieve this a vibration fault
simulation rig (VFSR) is designed and constructed so as to simulate and analyze some
of the most common vibration signals encountered in rotating machinery. Vibration
data are collected from the piezoelectric accelerometers placed at locations that
provide rigid vibration transmission to them. Both normal and fault signals are
analyzed using the singular value decomposition (SVD) algorithm so as to compute
the parameters of the auto regressive moving average (ARMA) models. Machine
condition monitoring is then based on the AR or ARMA spectra so as to overcome
some of the limitations of the fast Fourier transform (FFT) techniques. Furthermore
the estimated AR model parameters and the distribution of the singular values can be
used in conjunction with the spectral peaks in making comparison between healthy
and faulty conditions. Different fault conditions have been successfully simulated and
analyzed using the VFSR in this paper. Results of analysis clearly indicate that this
method of analysis can be further developed and used for self-diagnosis, predictive
maintenance and intelligent-based monitoring
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