The design and analysis of multivariable FUZZY logic feedback CONTROL systems are presented in this paper. A general design procedure is discussed and a multi-dimensional rule base in the form of the FUZZY RELATIONAL Matrix (FRM) developed. The decision-making components of the FUZZY Logic CONTROLler(FLC) are not explicitly based on the conventional FUZZY Associative Memory (FAM). The compositional rule of INFERENCE is employed to transform the set of FUZZY rules into the corresponding I;RM"s. A FUZZY connective OR (Maximum) is operated on the set of derived FRM"s to obtain a final FUZZY matrix which essentially constitutes the rule base of the FLC. This matrix establishes the relationship between a given CONTROLler input with every CONTROLler output. Hence, instead of using a forward changing technique for firing the applicable rule, an unknown output is evaluated by the composition of the related input with the FRM. Different MIMO CONTROL system configurations are investigated and direct interaction CONTROL as well as the cross-coupled FUZZY CONTROLlers implemented. Results of extensive simulation studies of two linked tanks are presented to illustrate the usefulness of the proposed approach.

In this paper, two sets of sufficient conditions are obtained to ensure the existence and stability of a unique equilibrium point of unforced first order FUZZY RELATIONAL dynamical systems by using two different approaches which are both based on the FUZZY RELATIONAL matrix of the model. In the first approach, the equilibrium point of the system is one of the centers of the related membership functions. In the second approach, the equilibrium point of the system is the origin (the center of the middle membership function) and the behavior of the system, though can be nonlinear, is symmetric around the origin. The results are approved by numerical examples.

Precise temperature regulation is essential in various industrial applications, particularly in environments requiring high accuracy and stability, such as egg incubation. Conventional CONTROL strategies, including Proportional-Integral-Derivative (PID) CONTROLlers and FUZZY INFERENCE Systems (FIS), often exhibit limitations in handling nonlinearities, disturbances, and uncertainties. To address these challenges, this research proposes a Fractional FUZZY INFERENCE System-based PID (FFIS-PID) CONTROLler, which enhances the adaptability and robustness of temperature CONTROL mechanisms. Unlike traditional FUZZY systems that rely solely on membership degrees, FFIS introduces fractional membership functions and fractional indices, enabling a more flexible and dynamic interpretation of FUZZY rules. The key innovation lies in the fractional compositional rule of INFERENCE, which allows the system to intelligently balance the influence of rules by adjusting their impact based on both the truth degree and the information volume. This enhances the adaptability of the CONTROL strategy without altering the fundamental rule base structure. The study involves designing fractional membership functions, selecting optimal fractional indices, and evaluating their effects on system behavior. A comparative analysis between FIS-PID and FFIS-PID CONTROLlers is conducted through simulations and experimental validation on an incubator system. The results confirm that the FFIS-PID CONTROLler provides superior temperature regulation by enabling real-time adaptability to changing conditions. This work contributes to the field of intelligent CONTROL by providing a novel approach to FUZZY INFERENCE enhancement through fractional compositional rule of INFERENCE mechanism. Future research could extend this methodology to other nonlinear CONTROL applications, further leveraging fractional indices for improved decision-making and stability.

One of the issues of reliable performance in the power grid is the existence of electromechanical oscillations between interconnected generators. The number of generators participating in each electromechanical oscillation mode and the frequency oscillation depends on the structure and function of the power grid. In this paper, to improve the transient nature of the network and damping electromechanical fluctuations, a decentralized robust adaptive CONTROL method based on dynamic programming has been used to design a stabilizing power system and a complementary static var compensator (SVC) CONTROLler. By applying a single line to ground fault in the network, the robustness of the designed CONTROL systems is demonstrated. Also, the simulation results of the method used in this paper are compared with CONTROLlers whose parameters are adjusted using the PSO algorithm. The simulation results show the superiority of the decentralized robust adaptive CONTROL method based on dynamic programming for the stabilizing design of the power system and the complementary SVC CONTROLler. The performance of the CONTROL method is tested using the IEEE 16-machine, 68-bus, 5-area is verified with time domain simulation.

Non-cooperative intelligent CONTROL agents (ICAs) with dedicated cost functions, can lead the system to poor performance and in some cases, closed-loop instability. A robust solution to this challenge is to place the ICAs at the feedback Nash equilibrium point (FNEP) of the differential game between them. This paper introduces the designation of a robust decentralized infinite horizon LQR CONTROL system based on the FNEP for a linear time-invariant system. For this purpose, two CONTROL strategies are defined. The first one is a centralized infinite horizon LQR (CIHLQR) problem (i.e. a supervisory problem), and the second one is a decentralized CONTROL problem (i.e. an infinite horizon linear-quadratic differential game). Then, while examining the optimal solution of each of the above strategies on the performance of the other, the necessary and sufficient conditions for the equivalence of the two problems are presented. In the absence of the conditions, by using the least-squares error criterion, an approximated CIHLQR CONTROLler is presented. It is shown that the theorems could be extended from a two-agent CONTROL system to a multi-agent system. Finally, the results are evaluated using the simulation results of a Two-Area non-reheat power system.

It is essential to CONTROL polymerization parameters in order to reach a specific polymer. A FUZZY CONTROLler is proposed to CONTROL temperature using reactor and jacket deviations. However, uncertainty exists on a jacket temperature, due to noise and disturbance effects. FUZZY numbers are applied to model this uncertainty. Consequently, a FUZZY trajectory is derived for jacket temperature. Finally, a pseudo-Sugeno FUZZY CONTROLler is designed for temperature tracking. The results show the good performance of this CONTROLler for CONTROL of solution polymerization of methyl methacrylate.

Multivariable liquid level CONTROL is necessary in process industries to ensure quality of the product and safety of the equipment. However, the significant problems of the CONTROL system include excessive time consumption and percentage overshoot, which result from ineffective performance of the tuning methods of the PID CONTROLlers used for the system. In this paper, FUZZY logic was used to tune the PID parameters to CONTROL a four-coupled-tank system in which liquid level in tanks 1 and 2 were CONTROLled. Mass Balance equation was employed to generate the transfer function matrix for the system, while a FUZZY INFERENCE System (FIS) file is created and embedded in FUZZY logic CONTROLler blocks, making tuning rules for the PID. Matlab R2009b simulation of the system model shows that the rise time (RT), settling time (ST), peak value (PV) and percentage overshoot (PO) for the developed DF-PID CONTROLler were 1. 48 s, 4. 75 s, 15 cm and 0% respectively for tank-1; and 0. 86 s, 2. 62 s, 10 cm and 0% respectively for tank-2, which are the smallest and best values when compared with other PID tuning methods namely: Ziegler-Nichols, Cohen-Coon and Chien-Hrones-Reswick PID tuning methods.

In this study the possibility of using FUZZY INFERENCE system efficiency, creating a bridge between meteorological, plant parameters, and Daily Yield, and comparing the accuracy of Daily Yield using these systems were investigated. After analyzing the different models and different combinations of daily meteorological data, seven models for estimating daily Yield were presented. For these models, the calculated Yield from AQUACROP model was considered as a base and the efficiency of other models was evluated using statistical methods such as root mean squared error, error of the mean deviation, coefficient of determination, Jacovides (t) and Sabbagh et al. (R2/t) criteria. An experiment was carried out during the 2014-2015 growing season in the Agricultural Research and Education Center of Khorasane Razavi province using a randomized complete block design with a split plot arrangement and four replications. This experiment was including of three irrigation levels treatments as the main plot and three method of planting treatments (transplanting 20-days, transplanting 30-days and direct seeded) as subplots. From the available data, 75 percent was used for training the model and the rest of 25 percent was utilized for the testing purposes. The results derived from the FUZZY models with different input parameters as compared with AQUACROP model showed that FUZZY systems were very well able to estimate the daily Yield. FUZZY model so that the highest correlation with the 9 input variables (r=0. 98) had in mind and evaluate other parameters, the model with 2 parameters, match very well with the AQUACROP model had stage training. In the test phase, training phase was very similar results and the model with the second phase of harvest index and canopy cover will get the best match. According to the results of this study it can be concluded that FUZZY model approach is an appropriate method to estimate the daily yield.

Corporate growth is an important factor for economic growth. Therefore, choosing the right growth strategy is one of the most important issues in managerial decisions. Accordingly, the purpose of this study was to design a business growth strategy selection model based on Ansoff matrix using FUZZY INFERENCE system. To achieve the research goal, first based on the ANSF matrix, Factors affecting market product strategy selection are identified and then two-level FUZZY INFERENCE system designed to select the company's growth strategy. The statistical population of this research was chemical and chemical industries of East Azarbaijan province. The statistical sample is based on the sample of available 124 managers of these companies. A questionnaire was used to collect the data, which was distributed among the sample members after determining its validity and reliability. In order to analyze the data while using exploratory factor analysis, a FUZZY INFERENCE system based on triangular membership functions and Mamdani INFERENCE is used to formulate the model of choosing the growth strategy of the company. The research results show that the designed system is able to identify the right growth strategy for companies. Also, the results in determining the growth strategy of the companies show that for the surveyed community, the best growth strategy was market development.

The proximate analysis is the most common form of coal evaluation that reveals the quality of a coal sample. It examines four factors including moisture, ash, volatile matter (VM), and fixed carbon (FC) within the coal sample. Every factor is determined through a distinctive experimental procedure under ASTM specified conditions. These determinations are time consuming and require various laboratory equipment. The calorific value is one of the most important properties of a solid fuel and its experimental determination requires special instrumentation and highly trained operator. This paper develops mathematical and ANFIS models for estimation of two factors of proximate analysis based on the other two factors. Furthermore, the estimation of calorific value of coal samples based on proximate analysis factors is performed using multivariable regression, the Minitab 16 software package, as well as ANFIS and MATLAB software package. The results indicate that ANFIS is a more powerful tool for estimation of proximate analysis factors and calorific value than multivariable regression method. The following equation estimates the calorific value of coal samples with high precision: Calorific value (btu/lb)=12204 - 170 Moisture+46.8 FC - 127 Ash.