fuzzy-logic

Aviation Safety has always been one of the key and important topics in the aviation industry. Since the first human flight, aviation accidents and incidents worldwide have led to extensive efforts to improve flight safety. Identifying the factors contributing to these accidents and accurately analyzing them can greatly assist in reducing incidents and enhancing flight safety. In this regard, various models and theories have been developed to assess the factors influencing aviation safety. When sufficient historical data is unavailable, using fuzzy logic and fuzzy analytical hierarchy process (FAHP), in combination with expert opinions, can be an appropriate alternative for evaluating the technical factors affecting aviation safety. This study employs the fuzzy analytical hierarchy process (FAHP) to evaluate the technical components influencing aviation safety. For this purpose, the five-part classification from the International Air Transport Association (IATA), including human, technical, environmental, organizational, and incomplete information factors, was used as the main criteria, with 36 sub-criteria. Specifically, the findings show that technical and human factors hold the highest weight among the other criteria.  another significant finding of this study is the high impact of incomplete information on reducing aviation safety levels. The lack of access to complete and up-to-date data can affect decision-making processes and lead to increased risks. By employing FAHP, aviation organizations can focus on key factors and implement effective safety improvement programs through precise prioritization.

Engine life prediction has always been of interest in the automotive industry. Wear is the main factor in limiting the life of an engine. Wear is considered among the parts with a lot of reciprocating movement in the engine. Piston rings and liners with bearings are the most important wear parts in the engine. In this article, the wear pattern in the rings and cylinders of the same family of engines has been investigated after 500 hours of durability tests. The rating results of these parts after the durability test are considered wear criteria. Then, these qualitative data are related to the parameters of blowby and the amount of engine oil consumption in the last hours of the test by the fuzzy logic method. Finally, a criterion for measuring wear is provided without the need to disassemble and rate the engine. The application of this criterion is to plan future tests on used engines without disassembling them and according to their wear rate. The important achievement of this method is saving the cost and time of engine preparation according to the amount of wear.

Bone micromilling is widely used in orthopedic, spine, skull, knee joint replacement, and orthopedic surgeries to cut bone and make holes in tissue. The application of tools with a smaller diameter in the bone micromilling compared to conventional milling causes a significant reduction in force and also the length of the treatment period. In this article, in the form of an experimental study, an intelligent model for predicting bone micromilling force has been obtained based on the fuzzy inference system. For this purpose, first, an experiment design method has been used to extract a group of practical experiments. Then, based on the obtained results and approximation capability of fuzzy systems, an accurate model for predicting the cutting force has been established founded on the input values of tool rotation speed, feed rate, tool diameter, cutting depth and cutting direction. By examining the obtained results, it can be seen that the fuzzy model has been able to accurately approximate the resulting force of the bone micromilling process based on the considered inputs; The mean absolute percentage error and coefficient of determination for the data of the test section were calculated as 11.22% and 0.93%, respectively. Using the data of this research, surgeons with full knowledge can set the best values of the input variables of the micromilling process without worrying about causing damage and cracks in the bone tissue with the maximum possible operating speed.

In a holistic design; Creating a common language between different engineering, taking in to account the designer/customer's interests and preferences, removing potential shortcomings of common design methodologies and taking full advantage of the benefits of MDO approaches are the most important factors for achieving logical and realistic results. In this paper, a novel Comprehensive Preference-based Design approach is presented which attempts to achieve subjective attributes that are defined in the concept of maximization of designer/customer's satisfaction in addition to objective goals which are formulated in the form of minimization of a performance criterion in a two-phase structure using two nested optimizers. In the first phase of CPD, using the concept of satisfaction, the subjective preferences of the designer/customer are defined in terms of fuzzy relationships and operators in the form of demands and desires. Whereas the results of this phase are inaccurate, in the second phase, it is attempted to define a performance criterion and in order to achieve an optimal operational plan, attitude parameters and the compromises needed to meet the designer/customer's preferences are implemented. The methodology is utilized to design of a UAV with flight duration of 24 hours, 45 m/s of cruise speed at least, payload of 200 kg and less than 1 km take-off distance. To evaluate the results of CPD, the design process using MDO in AAO framework is also performed. Comparison of the results shows that despite the higher mass of UAV designed by CPD, overall satisfaction is higher and preferences have been satisfied.

The main objective of this article is to apply the control allocation approach for the landing phase of the F/A-18 aircraft. For this purpose, the non-linear three-degree-of-freedom model of the aircraft is used, and the intelligent control allocation approach, based on fuzzy logic, is utilized to design the longitudinal flight control system. The actuators involved in the aircraft landing process are the elevator angle and the thrust vector control angle of the aircraft engine. By allocating control signals between the two mentioned actuators, the plane starts the process of lowering the height and finally reaches the ground level. To improve the efficiency of the fuzzy controller, reduce the control effort and increase the accuracy and quality of the landing, the genetic algorithm based on the NSGA-II method is used and the variables of the fuzzy controller are modified. The results obtained from the simulation show that the proposed control allocation approach has a high ability to control and stabilize the aircraft in the landing process. Also, the output variables converge to a desired value and the aircraft completes the landing process with proper accuracy and low control effort.

The anti-lock braking system (ABS) adjusts the longitudinal force of the tire by setting the coefficient of longitudinal slip of the car in its optimum value. This applies the proper braking torque to the car and in addition to reducing the car's stopping distance, prevents the wheels from locking. Due to the uncertainty and road conditions, the maximum length of the tire varies, so it is not appropriate to use a fixed value for the desired longitudinal slip. To solve this problem, in this paper, the desired longitudinal slip value is obtained at any moment using the sliding mode-based extremum seeking algorithm in combination with fuzzy method according to the road conditions to maximize the tire's braking force. Then the nonlinear prediction-based controller finds the right amount of braking torque at any moment by adjusting the longitudinal slip in the calculated desired value. The simulation is performed on a nonlinear vehicle model and the results of this simulation show the proper performance of the controller and the effect of using the desired instantaneous longitudinal slip to reduce the stopping distance.

The controller of the hybrid electric vehicle determines the combustion engine start-stop time, the operation points, and regenerative brake energy. The Controlling approach of hybrid electric vehicles controls the amount of needed fuel in every driving situation. In the present study, the thermostat strategy is implemented along with fuzzy logic control and compared to the classic thermostat strategy. The fuel consumption is compared in two different strategies. GT-power and Simulink software are implemented to simulate the series hybrid electric model. The numerical model is compared and validated with experimental data. The validated numerical model calculates the vehicle fuel consumption in the new European driving cycle. Results show that the use of fuzzy logic control reduces the fuel consumption of series hybrid electric vehicle 4 percent compared to the classical control strategy.

In this paper, a novel Comprehensive Preference-based Design (CPD) approach is presented which attempts to achieve subjective attributes that are defined in the concept of maximization of designer/customer's satisfaction in addition to objective goals which are formulated in the form of minimization of a performance criterion in a two-phase structure using two nested optimizers.In the first phase of CPD,using the concept of satisfaction,the subjective preferences of the designer/customer are defined in terms of fuzzy relationships and operators.Whereas the results of this phase are inaccurate,in the second phase,it is attempted to define a performance criterion and in order to achieve an optimal operational plan,attitude parameters and the compromises needed to meet the designer/customer's preferences are implemented.The methodology is utilized to design of a space launch vehicle for delivering 1200 kg payload to a 750 km orbit.Comparison of the results shows that despite the higher mass of launch vehicle designed by CPD,overall design satisfaction is higher and designer/customer's preferences have been satisfied.

This research tries to propose a method to solve problems related to constrained multi-objective optimizations (implementation, computation time, and simplicity). This method, based on fuzzy logic, converts constrained multi-objective optimization problem into unconstrained single-objective optimization problems so many of the mentioned problems are solved. To demonstrate the efficiency of this method, three multidisciplinary design optimizations of an unmanned aerial vehicle have been performed. The aim of the first optimization is to compare the performance of the proposed method with two well-known methods of multi-objective optimizations. The purpose of the second and third optimizations is to show this capability of the proposed method that the designer, according to need, can consciously change the degree of importance on the objective functions or constraints. The results of the optimizations show that the computational time has been reduced, and two different optimal designs have been obtained by changing the degree of importance.

Lane changing behaviors, due to the hidden aspects of factors involved, are considered as one of the most complicated behaviors in traffic flows. This paper proposes an intelligent system for discretionary lane change behaviors in the real traffic flow. The introduction presents a review of the research into the models of lane change behaviors. Then, a new method is proposed to determine the initial and final points of lane changing behavior in terms of time parameter. Hypotheses are then introduced to extract data on lane change behaviors from data recorded by NGSIM. An intelligent system is put forward using two control sub-systems based on fuzzy logic. Basically, this paper brings about innovative features by the utilization of time parameter during the execution of lane change behaviors and by the consideration of the impact of human and environmental factors through measurable data such as relative distance and relative speed in terms of time parameter. Finally, the results of the proposed intelligent system are compared with the behaviors of the driver in real situations. The framework of the result analysis employs error criteria and standard deviation (Variance). The said comparison indicates that the developed intelligent system outperforms the human behaviors in real situations.

Due to the importance of tumbling mills in processing industries and factories and the lack of an acceptable model for identifying and predicting their performance, it is necessary to optimize these complexes, non-linear, and large systems. This paper aimed to study multi-objective optimization of operating parameters in a tumbling mill. To evaluate the effects of the mill working parameters such as mill speed, ball filling, slurry concentration, and slurry filling on grinding process, power draw, wear of lifters and size distribution of the mill product, it was tried to manufacture a pilot model with a smaller size than the actual mill. For this aim, a mill with 1×0.5m was implemented. The feed of the mill is copper ore with a size smaller than 1 inch. The experiments were done at 65 to 85% of the critical speed. In addition, the combination of the balls was used as grinding media with 10 to 30% of the total volume of the mill. Slurry concentration is 40 to 80% (the weight fraction of solid in slurry) and the slurry filling is between 0.5 and 2.5. In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) based multi-objective optimization (NSGA-II) of tumbling mill is done. Level diagrams are used to select the best solution from the Pareto front. The results showed that the best grinding occurs at 70-80% of the critical speed and ball filling of 15-20%. Optimized grinding was observed when the slurry volume is 1-1.5 times of the ball bed voidage volume and the slurry concentration is between 60 and 70%.

In this paper, the hybrid control of the formation flying of spacecrafts has been investigated. The trajectory deflection of space asteroids, which are potentially life-threatening on Earth, are being actively pursued in recent scientific researches. To accomplish this mission, several methods have been proposed to date, in which case the use of gravity tractor is an indicator and hence the method is used in this paper. The formation flight of spacecrafts technology is a function of the relative dynamic equations, which are also used for its active control. In this way, the PID controller, which is widely used in various industries and inherently has robust properties, has been used as a base controller, and the fuzzy control has been used to improve its adjustment, which results in the results obtained. From simulation, the performance of the combined controller performance is effective.

Today, Production in the shortest possible time, with the lowest cost and highest quality is the one of the most important criterions in the industry. Tube hydroforming (THF), because of its unique feature, increasingly used in military, industries, automobile manufacturing, aerospace, and bicycle industries. Most tube hydroformed parts are produced by a multistage loading process which the suitable loading condition in THF with regard to internal pressure and axial feeding should be designed to improve formability and to avoid failure modes on the final hydroformed product. This study deals with the procedure for the determination of the proper loading path in THF using a fuzzy logic control algorithm, which avoids the failure of the tube due to excessive induced strain during the forming process. For this purpose, Mamdani fuzzy control algorithm was used in conjunction with Abaqus FEA code for simulation of the THF process. The wrinkling measurements and the thickness variation obtained from simulation are used as input in fuzzy control and fuzzy control outputs are used to set the loading path. A controlled algorithm is designed to maximize the expansion of the tube and minimize the thickness variation of the wall and wrinkling at the same time. The numerical results were verified and validated by conducting experiments where a good agreement was obtained between the experimental results and simulations