A New Integrated Fuzzy Multi-Criteria Decision Model for Performance Evaluation

The perspective of competition in the high technology industry has changed impressively over the last two decades and the indicators which can be defined as the traditional indicators of business performance are insufficient today. So we have identified a new set of financial and non-financial performance indicators that can be used by firms and then, we developed a business performance measurement model. There may be relations and dependencies among the dimensions of performance. For this reason, performance evaluation should be conducted in a holistic manner. In this study, a hybrid method, Equated Priority Values (EPV), has been used to reflect the outcomes of the most commonly used approaches, including the modified Fuzzy Logarithmic Least Squares Method (modified fuzzy LLSM), Chang’s Extent Analysis Method and Mikhailov’s Fuzzy Prioritization Approach. A real world application is carried out to illustrate how the model can be utilized. The application could be interpreted as demonstrating the effectiveness and feasibility of the proposed model

Fractional order super-twisting sliding mode observer for sensorless control of induction motor

Purpose To meet the need of reducing the cost of industrial systems, sensorless control applications on electrical machines are increasing day by day. This paper aims to improve the performance of the sensorless induction motor control system. To do this, the speed observer is designed based on the combination of the sliding mode and the fractional order integral. Design/methodology/approach Super-twisting sliding mode (STSM) and Grünwald–Letnikov approach are used on the proposed observer. The stability of the proposed observer is verified by using Lyapunov method. Then, the observer coefficients are optimized for minimizing the steady-state error and chattering amplitude. The optimum coefficients (c1, c2, ki and λ) are obtained by using response surface method. To verify the effectiveness of proposed observer, a large number of experiments are performed for different operation conditions, such as different speeds (500, 1,000 and 1,500 rpm) and loads (100 and 50 per cent loads). Parameter uncertainties (rotor inertia J and friction factor F) are tested to prove the robustness of the proposed method. All these operation conditions are applied for both proportional integral (PI) and fractional order STSM (FOSTSM) observers and their performances are compared. Findings The observer model is tested with optimum coefficients to validate the proposed observer effectiveness. At the beginning, the motor is started without load. When it reaches reference speed, the motor is loaded. Estimated speed and actual speed trends are compared. The results are presented in tables and figures. As a result, the FOSTSM observer has less steady-state error than the PI observer for all operation conditions. However, chattering amplitudes are lower in some operation conditions. In addition, the proposed observer shows more robustness against the parameter changes than the PI observer. Practical implications The proposed FOSTSM observer can be applied easily for industrial variable speed drive systems which are using induction motor to improve the performance and stability. Originality/value The robustness of the STSM and the memory-intensive structure of the fractional order integral are combined to form a robust and flexible observer. This paper grants the lower steady-state error and chattering amplitude for sensorless speed control of the induction motor in different speed and load operation conditions. In addition, the proposed observer shows high robustness against the parameter uncertainties. </jats:sec

A Comparative Study of Neural Networks and Fuzzy Systems in Modeling of a Nonlinear Dynamic System

The aim of this paper is to compare the neural networks and fuzzy modeling approaches on a nonlinear system. We have taken Permanent Magnet Brushless Direct Current (PMBDC) motor data and have generated models using both approaches. The predictive performance of both methods was compared on the data set for model configurations. The paper describes the results of these tests and discusses the effects of changing model parameters on predictive and practical performance. Modeling sensitivity was used to compare for two methods