ABSTRACT:This article presents an Internet-based remote laboratory for digital signal processor (DSP)-controlled induction motor (IM) drive. The remote experimental rig uses MATLAB/Simulink compatible dSPACE DS1104 signal processor to realize the control algorithm, current control, and PWM modulation. MATLAB RealTime Workshop (RTW) environment provides the real-time operation using a PC and I/O boards and, therefore, application files required for conducting the experiment in laboratory environment can be prepared with the RTW and dSPACE real-time interface (RTI). Also a graphical-user interface can be designed using the dSPACE ControlDesk Developer (CDD). However, for implementation of remote laboratory, interface software between local application and remote users should be developed, since the MATLAB Web Server (MWS) does not allow online access to some hardware such as DS1104 used in this study. For this purpose, an interface using the Python code is developed for remote automation of the ControlDesk. Furthermore, server-client communication software with Delphi programming language is developed for remote implementation of the experiment. Using the user-friendly interface, the Internet-based remote laboratory allows the students to conduct the experiment by changing the predefined control parameters online or uploading the controller designed by the user and then to observe system responses in numerical, graphical, or video format on the remote computer.

The electrocardiogram (ECG) is a biological signal that contains important information about the cardiac activities of heart. ECG signal plays a very important role in the diagnosis and analysis of heart diseases. ECG signal is corrupted by various types of noise such as electrode movement, strong electromagnetic effect and muscle noise. Noisy ECG signal has been extracted using signal processing. This paper presents a weak ECG signal denoising method based on fuzzy thresholding and wavelet packet analysis. Firstly, the weak ECG signal is decomposed into various levels by wavelet packet transform. Then, the threshold value is determined using the fuzzy s-function. The reconstruction of the ECG signal from the retained coefficients is achieved by using inverse wavelet packet transform. We carried out several experiments to show the effectiveness of the proposed method and compared the results with the traditional wavelet packet soft and hard thresholding methods for weak signal denoising. The results are satisfactory according to calculated the correlation coefficient.

Neural and fuzzy courses are widely offered at graduate and undergraduate level due to the successful applications of neural and fuzzy control to nonlinear and unmodeled dynamic systems, including electrical drives. However, teaching students a neurofuzzy controlled electrical drive in a laboratory environment is often difficult for schools with limited access to expensive equipment facilities. Therefore, computer simulations and dedicated software are needed to assist the students in visualizing the concepts and to provide graphical feedback during the learning process. In this article, an educational software is proposed for the neuro-fuzzy control of induction machine drives. The tool helps students learn the application of neuro-fuzzy control of electrical drives. The software has a flexible structure and graphical user interface. The neuro-fuzzy architecture, the motor and load parameters can be easily changed in the developed software. Neuro-fuzzy control performance of induction motors can be monitored graphically for various control structures and current controllers. ß

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In this paper, a Proportional-Derivative and Integral (PD-I) type Fuzzy-Neural Network Controller (FNNC) based on Sugeno fuzzy model is proposed for brushless DC drives to achieve satisfied performance under steady state and transient conditions. The proposed FNNC uses the speed error, change of error and the error integral as inputs. While the PD-FNNC is activated in transient states, the PI-FNNC is activated in steady state region. A transition mechanism between the PI and PD type fuzzy-neural controllers modifies the control law adaptively. The gradient descent algorithm is used to train the FNN in direct adaptive control scheme. Presented experimental results show the effectiveness of the proposed control system, by comparing the performance of various control approaches including PD type FNNC, PI type FNNC and conventional PI controller, under nonlinear loads and parameter variations of the motor.