H. Metin Ertunc

PID and state feedback control of a single-link flexible joint robot manipulator

In this work, a single - link flexible joint robot manipulator is constructed and controlled using various control techniques. The position and trajectory control is performed by PH) and State Feedback control methods for this system. The purpose of this study is to keep the rotate angle of the link at desired position and to eliminate the oscillation angle of end effectors. The experimental results were compared for each method. The control blocks required for the system are performed on Matlab - SIMULINK and applied dSpace 1103 control board. The experimental results of the system based on Pro and State Feedback controller are quite satisfactory.

Performance prediction of a CI engine using artificial neural network for various SME and diesel fuel blends

This study deals with predicting performance parameters and exhaust emissions of a four-stroke, four-cylinder, direct injection diesel engine fuelled with soybean oil methyl ester and its blends with jet fuel, marine fuel and No. 2 diesel fuel using Artificial Neural Network (ANN) approach. For this aim, an ANN model for the engine was developed using experimental data, and the performance of the ANN predictions was measured by comparing them with the experimental results. It was revealed that the ANN approach can accurately predict the performance parameters and exhaust emissions of the diesel engine using various diesel and biodiesel fuels.

Implementation of current mode fuzzy-tuning PI control of single phase UPS inverter using DSP

Current mode fuzzy-tuning proportional-integral (PI) control of UPS (Uninterruptable Power Supply) inverter is presented in this paper. Double loop current mode and fuzzy logic controller are combined in this control scheme. Double loop current mode control scheme includes two control loops as voltage and current. Output voltage and filter inductor current are used as feedback variables in voltage control loop and current control loop respectively. In voltage control loop, voltage feedback gain is adjusted by fuzzy-logic controller (FLC). The input variables of the FLC are the output voltage error and its derivative and the output variable of the FLC is voltage feedback gain. This control scheme is simulated using C++ and PSIM and implemented using Texas Instruments TMS320LF2407 DSP (Digital Signal Processor). Simulations and implementation are realized under linear, nonlinear and fluorescent loads. The results prove that this control scheme provides low total harmonic distortion (THD) and fast dynamic response

Real time DSP based PID and state feedback control of a brushed DC motor

In this paper, real time position, trajectory and speed controls are implemented for a serial excited brushed DC motor using PID and state feedback control methods. The purpose of this study is to keep the position and speed on desired references. The control blocks are designed on Matlab-Simulink and applied by F28335 DSP control board. The experimental results of PID and state feedback methods are compared and discussed.

Development of a voice-controlled home automation using Zigbee module

In this study, we aimed speech recognition-based remote control of home devices. The system was designed at two stages: speech recognition module and transmitter module that sends commands including fewer than two main headings. In the first stage speech recognition algorithm is implemented in MATLAB. Mel Frequency Cepstrum Coefficients (MFCCs) are used as a feature extraction method. Dynamic Timing Warping (DTW) is used as a feature matching method. After the recognition of speech commands entered, Arduino pin situation is changed via USB. The voltage change in pin is detected by the same pin connected to the transmitter Zigbee module. When the change is detected, Zigbee transmitter sends data to connecting on the desired device to be controlled the receiver Zigbee module via wireless network. When sending data detected by the receiver Zigbee module, 5V relay is triggered connecting on the device pins of the module. Thus, it was possible to successfully remotely control any device with speech commands using wireless network.

Prediction of bearing fault size by using model of adaptive neuro-fuzzy inference system

Condition monitoring of bearings faults which have vital importance in machines and detection of faults earlier have very big importance in terms of disruption of process. In this study, certain sizes artificial faults are generated by the laser beam on inner rings of bearing and vibration signals are obtained from these bearings in a shaft-bearing setup. It is aimed to diagnose the size of the defects occurring in the bearings by using adaptive neuro-fuzzy inference system (ANFIS) model in the study. After extracting the real-time features of obtained vibration data, they are multiplied by the specific weight and they are given as input to the generated classification model. It has been observed difference of features extracted from of 0.15 cm, 0.5 cm, 0.9 cm diameter inner ring faulty bearings created by the laser depending on size of faults. ANFIS classification model is developed by using these features and the size of the faults occurring in these bearings were calculated with an actual error 2.40 %. Then a error band are created with 0.1mm threshold value and it is observed that all the predicted values are inside this error band.

The effect of bearings faults to coefficients obtaned by using wavelet transform

In this study, artificial defects in various diameters are formed on inner race, outer race and ball bearing which are essential components of a bearing and vibration signals are collected by a data acquisition card from bearing-shaft setup. The signals acquired are decomposed from noise with wavelet transform; thus vibration signal resulting from normal operation of the system is obtained. The energy of noisy and noise-free signal is calculated and the wavelet coefficients that will be used in classifying are obtained. As a conclusion of experimental studies, the technique based on wavelet transform coefficients accomplishes the classifications of different bearings fault types successfully.

Tool wear condition monitoring in drilling processes using fuzzy logic

During the era of the rapid automation of the manufacturing processes, the automation of the metal cutting and drilling process, which is one of the most crucial stages in the industrial process, has become inevitable. The most important difficulty in the automation of machining process is time and production loss that occurs as a result of tool wear and tool breakage. In this study, a fuzzy logic based decision mechanism was developed to determine tool wear condition by using cutting forces. The statistical parameters of the cutting forces collected during the drilling operation have been determined as variables for the membership functions of the fuzzy logic decision mechanism. The system developed in this study, successfully determined the tool wear condition in drilling processes.

Autoregressive and Neural Network Model Based Predictions for Downlink Beamforming

In Time-Division-Duplex (TDD) wireless communications, downlink beamforming performance of a smart antenna system can be degraded due to variation of spatial signature vectors in vehicular scenarios. To mitigate this, downlink beams must be adjusted according to changing propagation dynamics. This can be achieved by modeling spatial signature vectors in the uplink period and then predicting them for new mobile position in the downlink period. This paper examines time delay feedforward neural network (TDFN), adaptive linear neuron (ADALINE) network and autoregressive (AR) filter to predict spatial signature vectors. We show that predictions of spatial signatures using these models provide certain level of performance improvement compared to conventional beamforming method under varying mobile speed and filter (delay) order conditions. We observe that TDFN outperforms ADALINE and AR modeling for downlink SNR improvement and relative error improvement with high mobile speed and higher filter order/delay conditions in fixed Doppler case in multipaths.