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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.

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, k_i 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.

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.

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.

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.

The aims of the paper are to improve the dynamic response of an induction motor based position servo system and to remove the chattering problem in the sliding mode control theory by using fuzzy logic principles. The obtained results are also compared with conventional sliding mode controller to show its performance.

The main method used for the research is to form a thin boundary layer neighboring the switching surface by using fuzzy logic. The sliding mode control law is inherently discontinuous naturally. Therefore, there are some difficulties such as so many switches occurring between the control bounds, which cannot be carried out by real controllers. Therefore, fuzzy logic is used in the thin boundary layer to determine the control signal current. Thus, the chattering is eliminated.

The results show that the designed controller has superior performance. But, there are also some difficulties. It is difficult to obtain fuzzy rules. The rules can be obtained by using genetic algorithms without expert's knowledge. However, sliding surface slope C can be optimized to increase system's dynamic performance.

A new boundary layer consisting of the fuzzy rules in the sliding mode control is formed.

This study aims to reveal consumer perceptions towards organic food, particularly emphasizing the confusion and vagueness in the public eye.

In total, 21 well-educated and relatively high-income consumers were selected as cases of the study. The knowledge about the organic food concept and organic food preferences were analyzed through the grounded theory analysis. In this analysis, ten categories which are shopping from the close circle, the product information attitude, do it yourself (DIY), true and false facts and dilemmas, information sources, the most preferred organic products, perception of organic food indicator, market place trends, organic food orientation reasons, and attitude toward the organic products/producers were detected as the main issues.

The authors chose the cases from well-educated people who have a relatively high income, and it is seen that the knowledge levels of consumers are low regarding organic foods. This low-level knowledge becomes apparent in confused and erroneous answers and actions. The confusion over the organic food concept and the institutional image appear as two main findings of the study. The authors named this false fact as organic confusion and dilemmas in this analysis. According to the study’s results, there is a considerable amount of confusion over healthy and natural food contexts.

This study aims to bring a comprehensive delineation to the general perception of the organic food concept in society. And it reveals a detailed feedback for the institutions and companies on how to solve organic food problems such as confusion, institutional image problem, and false facts. Although organic product knowledge is shown as a critical factor in many studies in the literature, the number of studies that examine this situation in depth is insufficient.

The main purpose of this study is to detect homogeneous housing market areas among 196 districts of 5 major cities of Turkey in terms of house sale price indices. The second purpose is to forecast these 196 house sale price indices.

In this paper, the authors use the monthly house sale price indices of 196 districts of 5 major cities of Turkey. The authors propose an autoregressive (AR) model-based fuzzy clustering approach to detect homogeneous housing market areas and to forecast house price indices.

The AR model-based fuzzy clustering approach detects three numbers of homogenous property market areas among 196 districts of 5 major cities of Turkey where house sale price moves together (or with similar house sales dynamic). This approach also provides better forecasting results compared to standard AR models by higher data efficiency and lower model validation and maintenance effort.

In this study, the authors could not use any district-based socioeconomic and consumption behavioral indicators and any discrete geographical and property characteristics because of the data limitation.

The finding of this study would help property investors for establishing more effective property management strategies by taking different geographical location conditions into account.

From the government side, knowing future rises, falls and turning points of property prices in different locations can allow the government to monitor the property price changes and control the speculation activities that cause a dramatic change in the market.

There is no previous research paper focusing on neighborhood-based clusters and forecasting house sale price indices in Turkey. At this point, it is the first academic study.