Recurrent neuro fuzzi controller power system stabilizer

Power system stabilizers (PSS) have been widely used to damp low frequency electromechanical oscillations which occur in power systems due to disturbances. If no adequate damping is available, the oscillation can increase and cause system separation. Power system stabilizers (PSS) are installed in power system generator to help the damping of power system oscillations. There are many approaches to enhance damping while extending the power stability limit. To improve power system stabilizer (PSS) design problem include optimal control ,adaptive and self-tuning control, PID control, robust control, variable structure control and intelligent control. In this paper the power stabilizer is based on Recurrent Neuro-fuzzy Inference System (RNFIS) design controller. In order to test the robustness of the proposed design procedure of the (RNFIS), simulations will be carried out for the three-phase to ground fault and 1- phase fault at the middle of one of the transmission line. After these simulations, we will compare the result between a lead-lag and recurrent neuro-fuzzy controllers to see their difference in disturbances. The optimal solutions will be compared where the expected result will show that the oscillations in time response of the machine speed and the rotor angle is damped more effectively when the recurrent neuro-fuzzy controller and applied to the system

Learning fuzzy inference systems using an adaptive membership function scheme

An adaptive membership function scheme for general additive fuzzy systems is proposed in this paper. The proposed scheme can adapt a proper membership function for any nonlinear input-output mapping, based upon a minimum number of rules and an initial approximate membership function. This parameter adjustment procedure is performed by computing the error between the actual and the desired decision surface. Using the proposed adaptive scheme for fuzzy system, the number of rules can be minimized. Nonlinear function approximation and truck backer-upper control system are employed to demonstrate the viability of the proposed method

Automatic construction of rules fuzzy for modelling and prediction of the central nervous system

The main goal of this work is to study the performance of CARFIR (Automatic Construction of Rules in Fuzzy Inductive Reasoning) methodology for the modelling and prediction of the human central nervous system (CNS). The CNS controls the hemodynamical system by generating the regulating signals for the blood vessels and the heart. The main idea behind CARFIR is to expand the capacity of the FIR methodology allowing it to work with classical fuzzy rules. CARFIR is able to automatically construct fuzzy rules starting from a set of pattern rules obtained by FIR. The new methodology preserves as much as possible the knowledge of the pattern rules in a compact fuzzy rule base. The prediction results obtained by the fuzzy prediction process of CARFIR methodology are compared with those of other inductive methodologies, i.e. FIR, NARMAX and neural networksPostprint (published version

Design of microstrip patch antenna for IEEE 802.16-2004 applications

This thesis presents microstrip patch antenna IEEE 802.16-2004 standards for microwave applications and WiMax. Narrow bandwidth (BW) is the main defect of microstrip patch antenna in wireless communication. The bandwidth can be improved by increasing the substrate thickness, and using air as substrate with low dielectric constant. The antennas were fabricated using FR4 board. Two types of microstrip antenna were used, the first was a single microstrip patch antenna and the second was using an air-gap technique as the dielectric between two antenna boards. The spacer of the air-gap has thickness of 2mm. It was made of wood to separate between the two boards. The transmission line model was used to get the approximate dimension for the design. Different parameters were obtained depending on the simulation and measurement. The Computer Simulations Technology (CST) software was used to simulate the design and the measurement was executed by Vector Network Analyzer (VNA). The two designs were compared to each other and found that some improvements were obtained on the air-gap technique. The bandwidth was improved by 4.51 % with air-gap technique and only 1.02 % with the single patch antenna

Adaptive Non-singleton Type-2 Fuzzy Logic Systems: A Way Forward for Handling Numerical Uncertainties in Real World Applications

Real world environments are characterized by high levels of linguistic and numerical uncertainties. A Fuzzy Logic System (FLS) is recognized as an adequate methodology to handle the uncertainties and imprecision available in real world environments and applications. Since the invention of fuzzy logic, it has been applied with great success to numerous real world applications such as washing machines, food processors, battery chargers, electrical vehicles, and several other domestic and industrial appliances. The first generation of FLSs were type-1 FLSs in which type-1 fuzzy sets were employed. Later, it was found that using type-2 FLSs can enable the handling of higher levels of uncertainties. Recent works have shown that interval type-2 FLSs can outperform type-1 FLSs in the applications which encompass high uncertainty levels. However, the majority of interval type-2 FLSs handle the linguistic and input numerical uncertainties using singleton interval type-2 FLSs that mix the numerical and linguistic uncertainties to be handled only by the linguistic labels type-2 fuzzy sets. This ignores the fact that if input numerical uncertainties were present, they should affect the incoming inputs to the FLS. Even in the papers that employed non-singleton type-2 FLSs, the input signals were assumed to have a predefined shape (mostly Gaussian or triangular) which might not reflect the real uncertainty distribution which can vary with the associated measurement. In this paper, we will present a new approach which is based on an adaptive non-singleton interval type-2 FLS where the numerical uncertainties will be modeled and handled by non-singleton type-2 fuzzy inputs and the linguistic uncertainties will be handled by interval type-2 fuzzy sets to represent the antecedents’ linguistic labels. The non-singleton type-2 fuzzy inputs are dynamic and they are automatically generated from data and they do not assume a specific shape about the distribution associated with the given sensor. We will present several real world experiments using a real world robot which will show how the proposed type-2 non-singleton type-2 FLS will produce a superior performance to its singleton type-1 and type-2 counterparts when encountering high levels of uncertainties.</jats:p