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Determination of the soil erodibility factor (K-factor) is a cumbersome and expensive undertaking in the effort to predict the soil loss rates. The percentage of soil particles less than 0.1 mm in diameter, the percentage of organic matter, the structural as well as the textural class and permeability are the most important factors constituting the soil erodi-bility factor. Various methods of direct measurement for indirect prediction using models have been introduced so far for the measurement of K- factor. Using the new topics in in-formation technology, in particular the fuzzy system including the Mamdani Inference Engine, Singleton Fuzzyfier and Centriod Defuzzyfier can determine the soil erodibility factor. The K values obtained with this method were compared with those of USLE method. Over 394 samples based on the Wischmeier nemograph as a database were in-cluded in this research work by using the fuzzy system. Using some actual data in the fuzzy system and comparing it with the K values attained with the USLE model by calcu-lation of the regression coefficient, the applicability of this system was revealed.

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The present article investigates the application of high order TSK (Takagi Sugeno Kang) fuzzy systems in modeling photo voltaic (PV) cell characteristics. A method has been introduced for training second order TSK fuzzy systems using ANFIS (Artificial Neural Fuzzy Inference System) training method. It is clear that higher order TSK fuzzy systems are more precise approximators while they cover nonlinearities better than zero and first order systems with the same number of rules and input membership functions (MF). However existence of nonlinear terms of the rules’ consequent prohibits use of current available ANFIS algorithm codes as is. This article aims to give a simple method for employing ANFIS over a class of simplified second order TSK systems and applies the proposed method on the nonlinear problem of modeling PV cells. Error comparison shows that the proposed method trains the second order TSK system more effectively.

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In this study, a robust controller is designed for fuzzy network control systems (NCSs) using the static output feedback. Delay and data packet dropout affect on the stability of network control systems, and therefore, the asymptotic stability condition is established considering delay and data packet dropout. Delay is time-varying while the lower and upper bounds for delay is defined, and the number of data packet dropout is unknown. Data drift is also an important phenomena that may occur when data is transmitted from sensors to the controller and from the controller to actuators. This phenomenon is modeled as a stochastic variable with a probabilistic distribution. For stability analyses, Lyapunov–Krasovskii functions, which depend on the limits of delay and data packet dropout, are used. Results of controller design are derived as Linear Matrix Inequalities (LMIs). A numerical example is adopted to show the effectiveness of the proposed approach.    

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This paper considers control of a laboratory Quadruple Tank System (QTS) in its non-minimum phase mode. This system is a well-known laboratory process suitable to illustrate the concepts of multivariable control methods. The objective of this paper was to design a controller based on combination of the sliding-mode and the state-feedback control methods using fuzzy logic. The proposed method takes advantage of the fast transient response of the sliding-mode controller and the zero steady-state error of the state-feedback controller. In other words, the fuzzy system uses the SMC when the QTS is in the transient mode and utilizes the SFC when it is near the steady-state mode. Hence, the advantages of both controllers have been used simultaneously. The switching between these two controllers is continuous and smooth based on a few simple fuzzy rules. Stability analysis of the proposed method is presented based on the Lyapunov stability direct method. Experimental results confirmed effectiveness of the proposed method as compared with the stand-alone controllers, especially when there are uncertainties in the system parameters.

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Today, fast and accurate fault detection is one of the major concerns in the industry. Although many advanced algorithms have been implemented in the past decade for this purpose, they were very complicated or did not provide the desired results. Hence, in this paper, we have proposed an emerging method for deep groove ball bearing fault diagnosis and classification. In the first step, the vibration test signals, related to the normal and faulty bearings have been used for both of the drive-end and fan-end bearings of an electrical motor. After that, we have employed the one dimensional Meyer wavelet transform for signal processing in the frequency domain. Hence, the unique coefficients for each kind of fault were extracted and directed to the adaptive neuro-fuzzy system for fault classification. The intelligent adaptive neuro-fuzzy system was adopted to enhance the fault classification performance due to its flexibility and ability in dealing with uncertainty and robustness to noise. This system classifies the input data to the faults in the race or the balls of each of the fan-end and the drive-end bearings with specific fault diameters. In the final part of this study, the new experimental signals were processed in order to verify the results of the proposed method. The results reveal that this method has more accuracy and better classification performance in comparison with other methods, proposed in the literature.

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High blood glucose levels in the body named diabetes can increase damage in kidneys, eyes, heart and etc. In this investigation, a novel TS fuzzy static output feedback control structure is proposed to regulate the blood glucose level in the pre-defined desired values for type 1 diabetes using exogenous intra-venous insulin delivery rate. To this end, a nonlinear delay differential equation framework is considered to model the blood glucose/insulin endocrine metabolic regulatory system. The governing equations of the blood glucose/insulin model are approximated by a TS fuzzy model and then the proposed static output feedback controller is designed for this TS model.

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This paper presents a completely practical control approach for quadrotor drone. Quadrotor is modelled using Euler-Newton equations. For stabilization and control of quadrotor a classic PID controller has been designed and implemented on the plant and a fuzzy controller is used to adjust the controller parameters. Considering that quadrotor is a nonlinear system, using classic controllers for the plant is not effective enough. Therefor using fuzzy system which is a nonlinear controller is effective for the nonlinear plant. According to the desire set point, fuzzy system adjusts the controller gain values to improve the performance of quadrotor and it leads to better results than classical PID controller. To study the performance of fuzzy PID controller on attitude control of the system, a quadrotor is installed to the designed stand. The system consists of accelerometer and gyroscope sensors and a microcontroller which is used to design fuzzy PID attitude controller for the quadrotor. Considering that the experimental data has lots of errors and noises, Kalman filter is used to reduce the noises. Finally using the Kalman filter leads to better estimation of the quadrotor angle position and the fuzzy PID controller performs the desired motions successfully.

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Control systems under normal conditions can provide a desirable performance. But when faults occur in the system, maintaining the appropriate operating condition is a difficult and often necessary matter. In fact, lack of timely fault detection in sensitive systems will lead to damage significant amounts of resources and information. As a result, a growing tendency in the field of fault detection in both scientific and industrial communities has been created. However, if the system under consideration is nonlinear, fault detection cannot be possible with linear methods. In this case the main difficulty is in accurately modeling of process which effects on the accuracy of fault detection and troubleshooting. Fuzzy systems theory, is an effective tool to deal with the complicated and uncertain situations. This paper has considered the problem of fault detection based on the modeling for nonlinear systems using interval type-2 fuzzy system. Our proposed method for fault detection is to create a confidence bound using the estimation of upper and lower bounds for the system output which can be done using a type-2 fuzzy system. Here, a residual signal is produced which determines the presence or absence of fault in the system. In this method in case of deviating the output graph of the control system from the estimated upper and lower bounds, the occurrence of fault can be detected. Finally, in order to show the capabilities of proposed method, the method is applied on three-tank and electro-hydraulic nonlinear systems and the results are very satisfying.

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Sliding mode control technique is one of the well-recognized non-linear control methods. This method has an advantage like robustness against uncertainties. However, chattering phenomenon constraints the performance of closed loop system. To increase its efficiency, a fuzzy compensator is used along with this method. The fuzzy compensator weights are updated by using adaptive rules. The adaptation rate acts as a controlling coefficient. Therefore, the bigger amount of it increases the adaptation speed of weights which leads to the improvement of closed loop system performance. As a result, the probability of instability of closed loop system increases, too. In this study, it has been proposed to use a parallel fuzzy system along with the main fuzzy system in order to control its weights' adaptation. Moreover, a non-linear model of the electro-hydraulic system has been introduced as a case study. Finally, the performance of closed loop system and the efficiency of the proposed methods have been investigated by using numerical simulations.

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Quadrotor is one the most popular models of unmanned aerial vehicles with four actuated propellers which has a simple, light weight, small mechanical structure and high maneuverability. However, its nonlinear under-actuated dynamics needs more advanced controllers for rejection of external disturbances, balancing and precise trajectory tracking. In particular, the under-actuated subsystem of the quadrotor's dynamics needs a fast response without overshoot and steady state error. In this paper, fuzzy fractional-order proportional-integral derivative (FOFPID) controller is designed for quadrotor control system using fuzzy and fractional order systems to improve response speed, tracking accuracy and system robustness respect to the conventional PID controller. Controller architecture of the under-actuated subsystem of the quadrotor's dynamics is designed based on the inner-outer loop control theory which is employed explicit and analytical inverse kinematic of system to connect the inner and outer loops. Also, dynamics of the motors and actuators saturation are considered in the quadrotor’s dynamics model and their effects are studied on the controllers' performance. In order to evaluate tracking performance of controllers, trajectory of an eight aerial maneuver is designed and controllers’ performance is assessed in the absence and presence of wind disturbance. Trajectory tracking accuracy of the controllers is studied according to the maximum absolute error and integral of absolute error criterions and is compared that shows the proposed FOFPID controller has successfully improved performance of the quadrotor system.

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In this article, a novel control of wheeled mobile robot based on machine vision is considered. One of the common methods for controlling such systems is the use of Model Predictive Control (MPC) algorithms. In these systems, the response speed of the control algorithm and the optimality of these are two basic factors for achieving the optimal performance. Also, the impossibility of achieving precise values of the robot parameters and their variation during the operation of the robot is an important challenge in the implementation of the controller, therefore, this paper focuses on real-time and robust MPC, so that it can ensure the system against uncertainties and environmental disturbances in addition to the optimal and real-time response. Hence, the optimization based on projection recurrent neural network (PRNN) has been used as an optimizer to reduce the calculation time cost. The combination of PRNN optimization with MPC leads to new formulation and constraints that are considered to be the article innovations. Finally, in order to verify the validity of the proposed algorithm, the robot passes through the corridor with the presence of obstacles, which is simulated in the V-REP software. The results show that the optimum control input speed has been increased in comparison with similar methods, and the optimal path selection by the fuzzy system in the presence of obstacles has been well suited.