Samsul Bahari Mohd Noor

A fuzzy logic based controller for an indirect vector controlled three-phase induction motor

This paper presents the theory, design and simulation of a fuzzy logic based controller used for an indirect vector controlled three-phase induction motor. The analysis, design and simulation of the fuzzy logic controller for IVCIM drive system are carried out based on the fuzzy set theory. The FLC algorithm has been simulated on Simulink Toolbox in Matlab. The performance of the proposed FLC has been investigated and compared to the results obtained from the conventional PI controller based drive at different operating conditions such as sudden change in load. The simulation results demonstrate that the performance of the FLC is better than that for the conventional PI controller.

Microcontroller performance for DC motor speed control system

The electric drive systems used in industrial applications are increasingly required to meet higher performance and reliability requirements. The DC motor is an attractive piece of equipment in many industrial applications requiring variable speed and load characteristics due to its ease of controllability. Microcontrollers provide a suitable means of meeting these needs. Certainty, part of the recent activity on microcontrollers can be ascribed to their newness and challenge. In this paper, implementation of the MC68HC11E9 microcontroller for speed control of DC motor fed by a DC chopper has been investigated. The chopper is driven by a high frequency PWM signal. Controlling the PWM duty cycle is equivalent to controlling the motor terminal voltage, which in turn adjusts directly the motor speed. Experimental results have been obtained that show the employment of microcontroller for speed control and over current protection of a DC motor.

A genetically trained simplified ANFIS Controller to control nonlinear MIMO systems

This paper presents a simplified ANFIS (Adaptive Neuro-Fuzzy Inference System) structure acting as a PID-like feedback controller to control nonlinear multi-input multi-output (MIMO) systems. Only few rules have been utilized in the rule base of this controller to provide the control actions, instead of the full combination of all possible rules. As a result, the proposed controller has several advantages over the conventional ANFIS structure particularly the reduction in execution time without sacrificing the controller performance, and hence, it is more suitable for real time control. In addition, the real-coded genetic algorithm (GA) has been utilized to train this MIMO ANFIS controller, instead of the hybrid learning methods that are widely used in the literature. Consequently, the necessity for the teaching signal required by other techniques has been eliminated. Moreover, the GA was used to find the optimal settings for the input and output scaling factors for this controller, instead of the widely used trial and error method. To demonstrate the accuracy and the generalization ability of the proposed controller, two nonlinear MIMO systems have been selected to be controlled by this controller. In addition, this controller robustness to output disturbances has been also evaluated and the results clearly showed the remarkable performance of this MIMO controller.

Harmonic reduction in a boost-star three-phase single switch power factor correction circuit operating in discontinuous conduction mode

A harmonic injection technique, which reduces the line frequency harmonics of the single switch three-phase boost rectifier, is implemented. In this method, a periodic voltage is injected in the control circuit to vary the duty cycle of the rectifier switch within a line cycle so that the fifth-order harmonic of the input current is reduced to meet the total harmonic distortion (THD) requirement. Since the injected voltage signal, which is proportional to the inverted ac component of the rectified three-phase line-to-line input voltages is employed; the injected duty cycle variations are naturally synchronized with the three-phase line-to-neutral input voltages.

Flexible bio-signals channels acquisition system for ECG and EMG application

Each nerves and muscles of human body will generate potentials, voltages, and electrical field strength and known as bio signals such as electrocardiograph (ECG), electromyography (EMG), electroencephalogram (EEG), and electrooculogram (EOG). The signals may be acquired through non-invasive surface Ag/Ag Cl applications although the generated signals exist in very low levels of voltages. All the bio signals may be obtained from basic designs of instrumentation amplifier with the help of Ag/Ag Cl electrodes. However, different frequency band, interference, amplitude and strength may give huge challenges to the acquisition system. This paper provides instrumentation amplification design, include with the design concept to allow for flexible channels. There are lots of advantages from the proposed design such as minimal additional cost for additional channels used and circuit design modification or replacement. To suits bio-signals frequency bandwidth need, digital filter design in Labview software is added, as a part of acquisition system and to discard unwanted signals outside frequency bandwidth.. For now, the design is able to demonstrate to acquire two bio-potentials signals; ECG and EMG signals.

Quantitative Feedback Theory control design using particle swarm optimization method

In this paper, a particle swarm optimization (PSO) method is proposed to design Quantitative Feedback Theory (QFT) control. This method minimizes a proposed cost function subject to appropriate robust stability and performance QFT constraints. The PSO algorithm is simple and easy to implement, and can be used to automate the loop shaping procedures of the standard QFT. The proposed method is applied to the high uncertainty pneumatic servo actuator system as an example to illustrate the design procedure of the proposed algorithm. The proposed method is compared with the standard QFT control. The results show that the superiority of the proposed method in that it can achieve the same robustness requirements of standard QFT control with simple structure and low order controller.

A simplified adaptive neuro-fuzzy inference system (ANFIS) controller trained by genetic algorithm to control nonlinear multi-input multi-output systems

This paper presents a simplified adaptive neuro-fuzzy inference system (ANFIS) controller to control nonlinear multi-input multi-output (MIMO) systems. This controller uses only few rules to provide the control actions, instead of the full combination of all possible rules. Consequently, the proposed controller possesses several advantages over the conventional ANFIS controller especially the reduction in execution time, and hence, it is more appropriate for real time control. A real-coded genetic algorithm (GA) was utilized to optimize the premise and the consequent parameters of the ANFIS controller, instead of the hybrid learning methods that are widely used in the literature. Accordingly, the necessity for the teaching signal required by other optimization techniques has been eliminated. Furthermore, the GA was employed to determine the input and output scaling factors for this controller, instead of the widely used trial and error method. Two nonlinear MIMO systems were chosen to be controlled by this controller. In addition, the controller robustness to output disturbances was also investigated and the results clearly showed the notable accuracy and the generalization ability of this controller. Moreover, the result of a comparative study with a conventional MIMO ANFIS controller has indicated the superiority of the simplified MIMO ANFIS controller.

An extensive review on accessing quality information

The WWW has become one of the fastest growing electronic information sources. In the Web, people are engaging in interaction with more and more diverse information than ever before, so that the problem of information quality is more significant in the Web than any other information system, especially considering the rate of growth in the number of documents. Yet, despite a decade of active research, Information quality lacks comprehensive methodology for its assessment and improvement and a few researches have presented practical way for measuring IQ criteria. This paper attempts to address some of the issues involved in information quality assessment by classifying and discussing existing researches of information quality frameworks. While many of pervious works have concentrated on a dimension of information quality assessment, in our classification has been considered three dimensions: criteria, models and validation methods of the models and criteria. This classification prepares a bed for developing practical and at the same time valid information quality model.

Wavelet network: online sequential extreme learning machine for nonlinear dynamic systems identification

A single hidden layer feedforward neural network (SLFN) with online sequential extreme learning machine (OSELM) algorithm has been introduced and applied in many regression problems successfully. However, using SLFN with OSELM as black-box for nonlinear system identification may lead to building models for the identified plant with inconsistency responses from control perspective. The reason can refer to the random initialization procedure of the SLFN hidden node parameters with OSELM algorithm. In this paper, a single hidden layer feedforward wavelet network (WN) is introduced with OSELM for nonlinear system identification aimed at getting better generalization performances by reducing the effect of a random initialization procedure.

Application of optimized Higher Order Sliding Mode control on MEMS optical switch

MEMS optical switches are used in optical networks in order to provide the desired bandwidth. This paper presents the results of an optimised Higher Order Sliding Mode (HOSM) position controller design. The algorithm requires time derivatives of the sliding variable. A Robust Exact Differentiator is used to estimate time derivatives of the sliding variable. A novel application of Particle Swarm Optimization (PSO) is introduced for optimal tuning of the HOSM controller. Robustness of the controller is investigated and the performance is compared with that obtained by a First Order Sliding Mode (FOSM) controller.