Emine Dogru Bolat

Development of remote control and monitoring of web-based distributed OPC system

This paper focuses on implementing remote control and monitoring of a web-based distributed OLE for Process Control (OPC) system. Remote monitoring and control of OPC-based systems realized at N different local control points on the Internet are achieved by using a distributed OPC (DOPC) architecture. In this proposed architecture, every local control center can control and monitor every other control point. DOPC architecture is developed by using an OPC standard created for the automation industry. This OPC was developed as an industrial standard using Microsofts Object Linking and Embedding (OLE) technology. OPC enables different control devices to communicate with each other by exchanging data. This proposed architecture permits different OPC-based process control architectures from a wide range of fields to communicate with each other. Thus, OPC-based local control systems located in different places can communicate with each other on the Internet without using bus architecture. The basic functions of the proposed system were implemented using the Delphi software package. Local control architectures developed at N points can communicate with each other and a remote control point on a dynamic web page constructed using Active Server Pages (ASP). Consequently, the proposed DOPC architecture allows the user to control and monitor local systems from any location with internet access, and to implement data exchange between N OPC points.

Comparison of Kalman Filter and Wavelet Filter for Denoising

This paper presents denoising the signal using wavelet filter and Kalman filter. The noise is zero mean and the variance value is 0.001. Kalman filter removes disturbances or faults from the signal by using initialization and propagation of error covariance statistics. Implementation of Kalman filter is impractical in large scale models as shown for the oscillator system. As an alternative wavelet filter has been used for the same system. Coiflet 2 which is orthogonal wavelet has been used. Soft thresholding has been applied. Decomposition is performed at level 9. The results of wavelet filter and Kalman filter are shown. Response of wavelet filter is better when compared with Kalman filter result

Using Current Mode Fuzzy Gain Scheduling Of PI Controller for UPS Inverter

This paper presents control of UPS inverter using fuzzy gain scheduling of PI controller. This control scheme has double loop current mode control scheme in core. This scheme includes two control loops as inner and outer control loops. Inner control loop uses inductor current of inverter filter as feedback while outer control loop uses inverter output voltage as feedback. Fuzzy logic controller (FLC) is used to adjust the voltage loop PI controller parameters. The voltage error and its derivative are used as input variables of the FLC. In this study, current mode fuzzy gain scheduling of PI controller are simulated digitally using PSIM and C++ under linear, nonlinear and fluorescent loads. And the results show that current mode fuzzy gain scheduling of PI controller can provide low THD and good regulation quality especially under nonlinear and fluorescent loads

Experimental Autotuning PID Control of Temperature Using Microcontroller

Experimental autotuning proportional-integral-derivative (PID) temperature control of oven using microcontroller is presented in this paper. Different types of autotuning PID controller methods have been applied to the oven designed as an experiment set. This experiment set is an FODPT (first order plus dead time) system. Relay (PI, PID) and integral square time error criterion (ISTE) set point (PI, PID) are used as autotuning PID method. To be able to control this system a digital signal processing card is designed using PIC 17C44 as microcontroller and ADS1212 as A/D converter. And the results are discussed to define which controller is the best for the oven

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

Microcontroller based temperature control of oven using different kinds of autotuning PID methods

This paper presents microcontroller based autotuning proportional-integral-derivative (PID) controller for an oven designed as an experiment set. Different types of autotuning PID controller methods have been examined. Proportional, P, control method has been applied first. Relay and integral square time error criterion (ISTE) tuning methods are used as autotuning PID method. For relay tuning method, proportional (P), proportional-integral (PI) and proportional-integral-derivative (PID) and for ISTE disturbance (PI, PID) have been used. These methods have been applied to the experiment set which is an FODPT (First Order Plus Dead Time) system. To be able to control this system a digital signal processing card is designed. PIC17C44 is used as microcontroller and ADS1212 is used as A/D converter. And the results are discussed to define which controller is the best for this experiment set.

Real time industrial application of single board computer based color detection system

In this study, real time industrial application of single board computer based color detection system is realized. In this system, BeagleBoard-xM as a single board computer, a USB camera, a conveyor belt and an LCD7 touch screen are used. OpenCV is used as an image processing library in this color detection system. The main goal of this study is to define the number of different colored packages passing on the conveyor belt according to their color. Then, real time results of the number of the packages and the total package number are displayed on the LCD7 touch screen. At the same time, the USB camera image of the related package on the conveyor belt is monitorized on the same touch screen. If no image of any packages is taken by the USB camera during 60 seconds, the system is turned off.

Comparison of wavenet and neuralnet for system modeling

This paper presents nonlinear static and dynamic system modeling using wavenet and neuralnet. Wavenet combines wavelet theory and feed-forward neuralnet, so learning approach is similar to neuralnet. The selection of transfer function is crucial for the approximation property and the convergence of the network. The purelin and the tansig functions are used as the transfer functions for neuralnet and the first derivative of a gaussian function is used as the transfer function for wavenet. Wavenet and neuralnet parameters are optimized during learning phase. Selecting all initial values random, but for wavenet, it may be unsuitable for process modeling because wavelets have localization feature. For this reason heuristic procedure has been used for wavenet. In this study gradient methods have been applied for parameters updating with momentum. Error minimization is computed by quadratic cost function for wavenet and neuralnet. Nonlinear static and dynamic functions have been used for the simulations. Recently wavenet has been used as an alternative of the neuralnet because interpretation of the model with neuralnet is so hard. For wavenet learning approach, training algorithms require smaller number of iterations when compared with neuralnet. Consequently, according to the number of training iteration and TMSE, dynamic and static system modeling with wavenet is better as shown in results.

A new hybrid stereovision-based distance-estimation approach for mobile robot platforms

Abstract In this paper, a new hybrid stereovision-based distance-estimation approach is proposed for indoor mobile robot platforms. To calculate the distances between the robot and object, triangulation method is used first. However, this technique is insufficient for objects located on the left or right side of the robot or the case of the stereo camera pair fixed to the robot with an angle down to the floor. Therefore, a new approach is proposed by adding a look-up table and curve-fitting methods to the triangulation technique. In this approach, the stereo camera pair is located on the robot with an angle down to the floor. In the experimental studies, an average accuracy rate of 97.69% is obtained. The Manhattan and Euclidean distances of objects that are not in the same line as the robot are also calculated. Average accuracy rates of 98.24% for Manhattan and 98.03% for Euclidean distances are achieved.

Distance estimation using stereo vision for indoor mobile robot applications

Real time and accurate estimation of the distance between the robot and the object or the distance between two different objects is critical for robot applications. Various sensors as laser, sonar or camera have been used for this process. Two sensors as a camera and laser are employed together in some studies in literature. Camera based studies propose different methods using single camera or two cameras. In this study, the distance between the cameras and the colored object (red colored object is accepted in this study) is calculated in pixels and cm using two cameras situated in the same axis. In this proposed study, the images taken from the left and right cameras are preprocessed first. Then, the objects in the environment are determined. The last process is to estimate the required distance. 150 measurements, including distances between 60 cm and 140 cm, are performed in the experimental studies and an accuracy success over %90 is achieved.