Farah Hani Nordin

Fuzzy bang-bang relay controller for satellite attitude control system

Two level bang-bang controllers are generally used in conjunction with the thrust reaction actuator for spacecraft/satellite attitude control. These controllers are fast acting and dispense time dependent; full or no thrust-power to control the satellite attitude in minimum time. A minimum time-fuel attitude control system extends the life of a satellite and is the main focus of this paper. Fuzzy controllers are favored for satellite control due to their simplicity and good performance in terms of fuel saving, absorbing non-linearities and uncertainties of the plant. A fuzzy controller requires a soft fuzzy engine, and a hardware relay to accomplish bang-bang control action. The work in this paper describes a new type of fuzzy controller in which the hardware relay action is configured in the soft fuzzy engine. The new configuration provides fuzzy decision-making flexibility at the inputs with relay like two-level bang-bang output. The new fuzzy controller is simulated on a three-axis satellite attitude control platform and compared with conventional a fuzzy controller, sliding mode controller and linear quadratic regulator. The result shows that the proposed controller has minimum-time response compared to other controllers. Inherent chattering associated with a two-level bang-bang controller produces undesirable low amplitude frequency limit cycles. The chattering can be easily stopped in the proposed fuzzy bang-bang relay controller, hence adding multi-functionality to its simple design.

Analysis on RF emission of electrical appliances

With the increased number of electrical appliances in our daily life, Radio Frequency (RF) emission of electrical appliances and its general effects have imposed a great challenge to researchers dealing with human health and power quality of electrical supplies. Even though studying the general effects of the RF emission from electrical appliances is important, it would be beneficial to study the characteristics of the generated signals and how they differ from one appliance to another. Hence, the aim of this paper is to investigate the signal structure and magnitude variation of the RF signals emitted from electrical appliances. Six electrical appliances were tested and their RF emission was recorded consecutively. The emitted signals were captured using an Active-loop antenna with a range of 1 KHz to 30 MHz and a spectrum analyzer of Agilent E4411B model. Experiment results showed that all the tested appliances emit noticeable and unique RF signals when operating.

Electrical field of electrical appliances versus distance: A preliminary analysis

Every household electrical appliance that is plugged in emits electric field even if it is not operating. The source where the appliance is plugged into and the components of household electrical appliance contribute to electric field emission. The electric field may cause unknown disturbance to the environment or also affect the human health and the effect might depends on the strength of the electric field emitted by the appliance. This paper will investigate the strength of the electric field emitted by four different electrical appliances using spectrum analyser. The strength will be captured at three different distances; (i) 1m (ii) 2m and (iii) 3m and analysis of the strength of the electrical field is done based on the three different distances. The measurement results show that the strength of the electric field is strongest when it is captured at 1m and the weakest at 3m from the electrical appliance. The results proved that the farther an object is located from the electrical appliance; the less effect the magnetic field has.

Layer-Recurrent Network in identifying a nonlinear system

Layer-recurrent network (LRN) is a dynamic neural network and is seen as a promising black box model in identifying a nonlinear system injected with nonlinear input signal. In this paper, LRN will be used to identify a nonlinear, state space 3-axis satellite model. Open loop identification is applied and methodology on nonlinear system identification is presented where the best pair of input and output data is first measured. Using the simulated data, six LRN models are used to identify the satellite dynamics. It is shown that only 200 epochs are needed to train a network to converge to a reasonable mean squared value (mse). LRN output is then compared with the state space model where it shows that LRN model is capable to produce similar results as the state space satellite model without knowing the systempsilas state and prior knowledge of the system.

Identification of electrical appliances using non-intrusive magnetic field and probabilistic neural network (PNN)

The electricity waste is severe especially in large organizational buildings where the use of air conditioners, fridges and electrical motors are rampant. Due to lack of energy saving consciousness, users may not switch off this equipment after use. Thus, it would be an advantage if there exist a system that will be able to identify the appliances from one place without the residence having to go and check the state of the appliance or without having to place various sensors intrusively. Since most electrical appliances emit magnetic fields, the paper proposes to use non-intrusive magnetic field signature waveforms to identify the type of appliance used. The magnetic field emitted by table fan, blender and hairdryer are chosen for this purpose. The magnetic field from these three appliances are collected from four different measurement distances i.e. (i) 0cm (ii) 10cm (iii) 30cm and (iv) 60cm. The features of the magnetic field are then extracted and trained offline using the Probabilistic Neural Network (PNN). Once trained, the PNN shows that it is able to successfully identify the appliances regardless of the measurement distance.

The implementation of key course concept in the evaluation of BEEE programme in UNITEN

Outcomes based education (OBE) has been implemented in UNITEN for 10 years since year 2006. Great efforts have been done in the formulation of the various levels of outcomes as well as the measurement of the attainments of the outcomes over the years. The process of evaluating the programme level Programme Outcomes (POs), in particular, has undergone significant changes as part of the CQI (Continual Quality Improvement) process in OBE. This paper presents an improved method of PO attainment using the Key Course concept in the Bachelor of Electrical and Electronic Engineering (BEEE) programme. Results show that the PO attainment correlates with attainment at students and course levels.

Classification of electrical appliances using magnetic field and probabilistic neural network

Many researches have proven that power lines and electrical appliances do emit electromagnetic fields and can be harmful to humans health. However, research on the effect of the magnetic fields on humans health is not yet conclusive. Instead of letting the magnetic fields emit by the electrical appliances be wasted, this paper aims to use the magnetic fields to classify or identify the electrical appliances being used. Table fans, blenders and hairdryers are the electrical appliances used for this purpose where they are divided into three different categories of usage i.e. (i) used less than 1year (ii) used between 1 to 5 years and (iii) used more than 5 years. The magnetic fields are measured from all the nine appliances. Then, the features of the magnetic fields are extracted and trained offline using the Probabilistic Neural Network (PNN). From the results, it is shown that the PNN is able to identify the type of electrical appliance being used regardless of the appliances years of usage using magnetic fields emitted by the appliances.

Measurement and estimation of electric field emission of a vacuum cleaner

Electric field emission of electrical appliances has become an important problem, especially when testing for safety and compliance with regulations of electromagnetic compatibility (EMC). To confirm the safety and compliance of an electrical appliance, it is important to measure the levels of the emitted electric and magnetic fields from this appliance and compare them to the exposure limit values set by the international standards. Moreover, modeling these emitted fields can aid understanding their characteristics and ease investigating how different systems react to such emission. However, a good model depends mainly on the accuracy and robustness of the measurement methodology. Hence, the aim of this paper is to present a measurement methodology and a frequency domain model for the emitted electric field of vacuum cleaners using system identification tools. The proposed model is a data-driven model where the recorded signal is used to construct the model using polynomial model estimation methods. Measurement setup, related work and the model equation are presented accordingly.

Detection of VLF and LF emissions of fluorescent light for efficient management of power consumption

In this research work, a detection probe of Very Low Frequency and Low Frequency (LF) emissions of fluorescent light is developed by using low cost loop antenna. The developed loop antenna is able to operate at VLF and LF bandwidth. The developed antenna is tested and measured with signal generator and oscilloscope in order to verify the usefulness of antenna. The developed antenna is subsequently used to detect the signal emitted by the fluorescent light. The antenna probe is located at different distance in order to obtain the peak voltage of received signal. Besides that, the fluorescent light is switch on and off respectively in order to verify the source of signal. From the oscilloscope, the received signal is operating at approximately 28 KHz. Hence, the developed antenna probe can be used for efficient management of power consumption as 28 KHz signal is detected if the light is on.

The effect of delays on the performance of Layer Recurrent Network

Layer Recurrent Network (LRN) is a dynamic network that has a feedback loop as well as a delay for each layer of the network except for the last layer. The main objective for this research is to study the effect of delays on the performance of the LRN in identifying a nonlinear model. A numerical experiment of the nonlinear model is set up before a set of input and output data is collected. The collected data is then used to train the LRN. The numbers of delays at the feedback loop is manipulated and the effect of the network performance is observed where it shows that the network has the best performance when the number of delay is set to more than the default/original value (which is one).