R2R controllers combine statistical process control and engineering process control. These techniques based on exponentially weighted moving average (EWMA) scheme and adjust input variable based on difference between forecasted and real output. This type of control consist some cost as difference inventory or overtime and etc.In this paper we suggest that first output controls by residual control chart, and if there is out of control case then the process is adjusted, also in this paper with considering cost parameters in adjustment of process, an economic model to optimum design of combination of R2R control and residual control chart is developed.

In the last few years, Run-to-Run (R2R) control techniques have been developed and used to control various processes in industries. These techniques combine response surface, statistical process control, and feedback control techniques. The R2R controller consists of a linear regression model that relates input variables to output variables using Exponentially Weighted Moving Average (EWMA). In this paper, we have developed a R2R controller model based on quality costs. The model consists of finding optimum weight of EWMA procedure in R2R controllers with respect to conformities and nonconformities costs. The validity and performance of the developed model were tested using a real case study in an optic industry application.  

A new methodology based on a functional link net (FLN) artificial neural network (ANN) is introduced for excitation control of a synchronous generator based on fuzzy power system stabilizer (FPSS). This method combines the advantages of the fuzzy controller along with the independence from model identification and fast processing ANN and proposes a new form of excitation controller for a synchronous generator. Delta-rule is used for training of ANN. ANN is trained for different load patterns which are produced by FPSS, and is continued until the total error was smaller than certain value. The analysis of a three-phase short circuit fault condition under various loading conditions of a single machine infinite bus system is presented to illustrate the application of the developed methodology. The obtained results show that the proposed FLN artificial neural network, based on fuzzy controller for power system stabilizer (PSS), can provide very good damping characteristic through wide rang of operating conditions for power system and it has smoother control for system variables; hence it improves dynamic operating of the system considerably.

The purpose of this study is to analyze oil and tax revenues in the Iranian economy. Applying the stochastic dynamic general equilibrium approach, a model for the oil exporting economy was simulated. In order to recover the tax system, oil and tax revenues added to the model as a part of government’s revenue. The present study applies Dynare software with Matlab to estimate the model’s parameters by Bayesian statistical techniques based on Monte Carlo method with Markov chain in the form of Metropolis-Hastings algorithm. The period of study is from 1368 to 1396 in a quarterly setting. In order to analyze the shocks, two scenarios were designed. In the first scenario, it was assumed that the government has oil revenues and all oil revenues are spent by the government so that the government does not rely on tax revenues. In the second scenario, it is assumed that 40% of the government's oil revenues are injected into the Development Fund and a percentage of it, is allocated to facilitate the production sector, and the government mostly relies on the taxes. The results show that the tax and oil shock by reducing dependence on oil and reliance on tax revenues, has negative impact on macroeconomic variables in the short term, but in the long run, with increasing tax revenues, production and, consequently, investment, consumption, employment has increased. JEL classification: H21 C13، E17، G5

Identification and analysis of geographic and climatic conditions for scientific researches and strategic studies is one of the main issues in countries. Nowadays, the use of Unmanned Aerial Vehicle because of its safety and high performance has been widely used in many countries. Exact action of this system leads to obtain accurate data and thereby achieve critical information. Therefore, an appropriate control system and perfect control of the drone is important. In this paper, an intelligent control using Fuzzy- Genetic Algorithm is presented on longitudinal flight dynamics of an Unmanned Aerial Vehicle. In this approach, all the membership functions and fuzzy rules are extracted from genetic algorithm method to minimize the over shoot and the steady-state error of the system. Finally, an example is illustrated to compare the proposed method with the classical PID controller.

Control of transmitted active power is an important issue in operation and management of power systems especially in congestion or fault conditions. In these situations, Thyristor Controlled Series Capacitor (TCSC) is used to continuous control and increase the transmitted power due to these facts that TCSC can act dynamically and is able to stable the system during fault conditions. In this paper, the transmitted power is controlled in the ten megawatt span by using the TCSC. For this purpose, various controllers such as PID, fuzzy and Adaptive Network-based Fuzzy Interface System (ANFIS) are designed to continuous control of the transmitted power. Simulation results evaluate advantages and disadvantages these controllers. ANFIS controller is designed by open loop method which has a good transient response. However, it has a large steady state error and is very sensitive to the variations in system. Fuzzy and ANFIS controllers are combined to remove these defects. The simulation results verify the advantages of the fuzzy-ANFIS controller with respect to the other designed controllers.

Any defect in the flight control system may cause an irreparable problem. Typically, a highly reliable system with human decision-making power is used to prevent or correct such errors in a flying vehicle. A fault tolerant control system is designed to deal with various types of errors that may occur in the system. Fault-tolerant control systems are divided into two main parts. The first part is the error detection and isolation phase and the second part is the control system design phase to overcome the error effects in the system, depending on the type of error and the location of the error, whether the sensor, actuator, or components, the control system must be able to eliminate error effects. In this paper, a neural-adaptive observer is used in the error detection stage, and in the second stage, a control system is designed based on the back-stepping algorithm. Nonlinear six-degree-of-freedom simulation results for an F-18 aircraft model indicate its suitable efficiency in the detection and compensation of fault effects.

The series robots have nonlinear dynamics with together uncertainties in joints’ forces and robot’s model. For the fuzzy control, two parameters of angular position and angular velocity are usually used as the linguistic variables. This method is similar the PD controller, and, consequently it cannot track a desired trajectory and it have a steady-state error. In the present paper the summation of errors is additionally used as a linguistic variable. Using three linguistic variables of error, error derivative and error integration for the angular position, trajectory tracking of a RR series robot is performed for its uncertain dynamic model. This method is similar the PID controller, and, consequently it can track a desired trajectory. Additionally, the presented fuzzy PID controller is not sensitive to the noise and uncertainty, and, it presents better results than a classic PID controller